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This chapter investigated tendering in stealth construction, emphasising innovative approaches and methodologies that prioritise environmental protection, safety, efficiency, and aesthetics. It began with an overview of the construction industry’s tendering processes, followed by an in-depth examination of various tendering types, including competitive and negotiated methods. The study highlighted contemporary trends such as electronic tendering, Building Information Modelling (BIM), green and sustainable procurement, risk management, data analytics, artificial intelligence, lean construction practices, and blockchain technology. Moreover, with a specific focus on stealth construction, the chapter further analysed certain criteria, including building cross-section development, visibility, radio frequency emission, and countermeasures. It explored integrating functional construction systems, including environmental, safety, health, and quality management. Additionally, it discussed methods like green building, modular construction, and low-impact techniques. Lastly, the chapter emphasised the strategies to achieve environmental protection, safety, speed, economy, and aesthetics in tendering for stealth construction.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.

The construction industry has long been criticized for unethical conduct. The owner usually manages the contractor's opportunistic behaviors by employing a professional supervisor, but there is a risk of covert collusion between the supervisor and contractor. Based on the principal–agent theory and collusion theory, this paper aims to investigate optimal collusion-proof incentive contracts.

This paper presents a game-theoretic framework comprising an owner, supervisor and contractor, who interact and pursue maximized self-profits. Built upon the fixed-price incentive contract, cost-reimbursement contract, and revenue-sharing contract, different collusion-proof incentive contracts are investigated. A real project case is used to validate the developed model and derived results.

This paper shows that the presence of unethical collusion undermines the owner's interests. Especially, the possibility of agent collusion may induce the owner to abandon extracting quality information from the supervisor. Furthermore, information asymmetry significantly affects the construction contract selection, and the application conditions for different incentive contracts are provided.

This study still has some limitations that deserve further exploration. First, this study explores contractor–supervisor collusion but ignores the possibility of the supervisor abusing authority to extort the contractor. Second, to focus on collusion, this paper ignores the supervision costs. What's the optimal supervision effort that the owner should induce the supervisor to exert? Finally, this paper assumes that the colluders involved always keep their promises. However, what if the colluders may break their promises?

Several collusion-proof incentive contracts are explored in a project management setting. The proposed incentive contracts can provide the project owner with effective and practical tools to inhibit covert collusion in construction management and thus safeguard construction project quality.

This study expands the organization collusion theory to the field of construction management and investigates the optimal collusion-proof incentive contracts. In addition, this study is the first to investigate the effects of information asymmetry on contract selection.

The present study aims to investigate the impact of the auditor’s opinion and internal control quality (ICQ) on future abnormal cash holdings for companies listed on the Tehran Stock Exchange (TSE).

Information about 216 companies in 2014–2021 was examined. This study used the absolute value of abnormal cash holdings to test the research hypotheses. However, future extra abnormal cash holdings and future deficit abnormal cash holdings were also tested. Modified multiple regression method and ordinary least squares (OLS) were used. The present study also applied the generalized method of moments (GMM) for endogeneity concerns.

The results showed that an unqualified audit opinion negatively and significantly affects a firm’s future abnormal cash holdings. Moreover, ICQ significantly strengthens the impact of an unqualified audit opinion on a firm’s future abnormal cash holdings. These results remained robust even after several robustness tests. This study tested the robustness of results through data division into the pre-COVID-19 and post-COVID-19 years. The test confirmed previous findings; however, the strength of these effects decreased in post-COVID-19 years.

Previous studies could not answer how an auditor’s opinion affects a company’s future abnormal cash holdings. Moreover, no empirical study has addressed the moderator role of ICQ in the relationship between unqualified audit opinion and future abnormal cash holdings. This study helps stakeholders evaluate the performance of firms more accurately, especially in any global health crisis such as the COVID-19 pandemic and similar crises. Combined with the research findings from developed countries, this study can potentially contribute to the global community’s efforts in advancing international objectives.

Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.

The present study aims to investigate the impact of manager conservatism on innovation and capital expenditures especially the moderating role of the crisis caused by the COVID-19 outbreak for companies listed on the Tehran Stock Exchange (TSE).

The present study investigated information about 178 companies in 2014–2022. In this study, the CEO’s signature was a psychological proxy of conservatism. Modified multiple regression analysis was used to investigate the moderating role of COVID-19. The present study also used the Generalized Method of Moments (GMM) to address endogeneity issues.

The results showed that manager conservatism has a positive and significant effect on capital expenditures, while manager conservatism negatively impacts firm innovation. In addition, COVID-19 significantly strengthens the impact of manager conservatism on capital expenditures and firm innovation. Hypothesis testing based on robustness checks confirmed these results.

This study aims to investigate the impact of manager conservatism, as a personality trait, on capital expenditures and innovation. These aspects have not been thoroughly explored in the existing literature. Additionally, it is important to consider the perceived threat of COVID-19, known to have a greater effect on conservatives. This study seeks to examine the interactive role of manager conservatism and COVID-19 on investment in capital expenditures and innovation. The present findings will not only benefit managers and policymakers in both developing and developed countries but will also provide valuable insights into decision-making processes related to capital expenditures and innovation during critical conditions such as the COVID-19 outbreak.

Determining the suitable material and accurate thickness of the thermal insulation layer used in exterior walls during the design phase of a building can be challenging. This study aims to determine suitable material and optimum thickness for the insulation layer considering both operational and embodied factors by a comprehensive assessment of the energy, economic and environmental (3E) parameters.

First, the energy model of an existing building was created by using Autodesk Revit software according to the as-built floor layout to evaluate the impact of five alternative insulating materials in varying thickness values. Second, using the results derived from the model, a thorough evaluation was conducted to ascertain the optimal insulation material and thickness through individual analysis of 3E factors, followed by a comprehensive analysis considering the three aforementioned factors simultaneously.

The findings indicated that polyurethane with 13 cm thickness, rockwool with 10 cm thickness and EPS with 20 cm thickness were the best states based on energy consumption, cost and environmental footprint, respectively. After completing the 3E investigation, the 15-cm-thick mineral wool insulation was presented as the ideal state.

This study explores how suitable material and thickness of insulating material can be determined in advance during the design phase of a building, which is a lot more accurate and cost-effective than applying insulating materials by assumed thickness in the construction phase.

To the best of the authors’ knowledge, this paper is unique in investigating the advantages of using thermally insulating materials in the context of a mosque structure, taking into account its distinctive attributes that deviate from those of typical buildings. Furthermore, there has been no prior analysis of the cost and sustainability implications of these materials concerning the characteristics of subtropical monsoon climate.

The metaverse has garnered increasing attention from researchers and practitioners, yet numerous firms remain hesitant to invest in it due to ongoing debates about its potential financial benefits. Therefore, it is crucial to analyze how the implementation of metaverse initiatives affects firms’ stock market value – an area that remains underexplored in the existing literature. Additionally, there is a significant lack of research on the contingency factors that shape the stock market reaction, leaving a noticeable gap in managerial guidance on the timing and benefits of investments in the metaverse. To narrow these gaps, we examine whether and when the implementation of metaverse initiatives enhances firms’ stock market value.

Based on 73 metaverse implementation announcements disclosed by Chinese listed firms during January 2021–August 2023, we employ an event study approach to test the hypotheses.

We find that metaverse implementation announcements elicit a positive stock market reaction. Moreover, the stock market reaction is stronger for technology-focused announcements and smaller firms, or when public attention to the metaverse is higher. Nevertheless, firms’ growth prospects do not significantly alter the stock market reaction.

This study extends the nascent literature on the metaverse by applying signaling theory to offer novel insights into the signaling effect of metaverse implementation announcements on stock market value and the boundary conditions under which the effectiveness of the signal varies. Besides, it provides managers with important implications regarding how to tailor the investment and information disclosure strategies of the metaverse to more effectively enhance firms’ stock market value.

Drawing upon socio-technical system theory, this study intends to investigate the effects of the congruence and incongruence between artificial intelligence (AI) and explorative learning on supply chain resilience as well as the moderating role of organizational inertia.

Using survey data collected from 170 Chinese manufacturing firms, we performed polynomial regression and response surface analyses to test our hypotheses.

We find that the congruence between AI and explorative learning enhances firms’ supply chain resilience, while the incongruence between these two factors impairs their supply chain resilience. In addition, compared with low–low congruence, high–high congruence between AI and explorative learning improves supply chain resilience to a greater extent. Moreover, organizational inertia attenuates the positive influence of the congruence between AI and explorative learning on supply chain resilience, while it aggravates the negative influence of the incongruence between these two factors on supply chain resilience.

Our study expands the literature on supply chain resilience by demonstrating that the congruence between a firm’s AI (i.e. technical aspect) and explorative learning (i.e. social aspect) boosts its supply chain resilience. More importantly, our study sheds new light on the role of organizational inertia in moderating the congruent effect of AI and explorative learning, thereby extending the boundary condition for socio-technical system theory in the supply chain resilience literature.

The operational carbon simulation in the building design stage is significant to life cycle carbon emissions. However, this process is challenging to reuse multi-source building information modeling (BIM) under different information availability. Thus, this study suggests an OpenBIM-based method for operational carbon simulation to integrate heterogeneous BIM models with different data accuracy, which can be applied across various stages of building design.

This study suggests a three-step method for operational carbon simulation using industry foundation classes (IFC). This method uses IfcSpace as the fundamental unit for limited data while using IfcDistributionElement for abundant data. Additionally, a case study proves the method's accuracy and efficiency by comparing it with existing tools.

Although the information availability is different, the simulation results of operational carbon are similar between schematic design and construction document design, with 1816.4 tCO2e/year and 1962.4 tCO2e/year, respectively. Furthermore, the case study shows a 16.9% carbon reduction through scheme optimization.

This study offers an approach that can directly utilize the multi-source heterogeneous BIM models to save time and reduce labor consumption. Using the space and the element as calculation units extends the simulation theory for evolving information accessibility across design stages.