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The primary objective of this study is to examine the effects of integrating human-centric technology on the overall effectiveness of sustainable building practises. This study intends to address the knowledge gap regarding the influence of human-centered factors on the adoption of technology in the construction industry. Additionally, it wants to establish a strong correlation between the integration of technology and the promotion of sustainability.

This research utilizes Partial Least Square Equation Modeling (SEM) as a comprehensive approach to analyze the intricate interrelationships within the building sector. This study examines a range of aspects, including “Change Management,” “Evaluation and Optimization,” “Integration and Coordination,” “Long-Term Planning,” “Safety and Compliance,” and “Training and User Engagement,” in order to evaluate their impact on the adoption of technology and the achievement of sustainable building outcomes.

The study reveals a robust and affirmative correlation between the use of human-centric technology and the attainment of success in the realm of sustainable building. The text elucidates the primary factors that influence the adoption of technology and underscores the need of adopting a comprehensive strategy that encompasses change management, adherence to safety standards, and optimization of resources.

This study contributes novelty by placing emphasis on the human-centric dimensions of technology adoption within the context of sustainable building. The results of this research provide a valuable contribution to the ongoing development of sustainable building practises and the adoption of new technologies in this sector.

The primary objective of this study is to analyze the correlation between the application of building information modeling (BIM), the construction project success (CPS) and project control. Additionally, this research aims to explore the potential mediating effect of project control on the link between BIM and construction project success in the Malaysian construction industry.

The present study employs a quantitative research methodology and adheres to a positivist research philosophy in order to examine the influence of BIM implementation on the CPS and project control within the Malaysian construction sector. The data were collected by structured questionnaire survey from 200 respondents using a purposive sampling strategy, and the results were analyzed by using partial least square (PLS)-structural equation modeling (SEM). The study's findings are all the more noteworthy since, despite the small sample size, 218 persons genuinely participated thanks to the purposive sampling approach.

The findings of this study indicate that there are notable and positive associations between various variables related to the implementation of BIM. These variables include accurate quantities and estimations, risk management, facility management integration, optimized resource allocation and real-time monitoring. Furthermore, these relationships are observed to have a significant impact on both CPS and project control. The study revealed that project control serves as a mediator in the relationship between the deployment of BIM and the CPS in the Malaysian construction sector, with the exception of its influence on risk management.

The originality of this research consists in examining the mediating function of project control in the relationship between BIM implementation and CPS. This study contributes to the existing knowledge by enhancing our understanding of the processes via which BIM positively influences project outcomes.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

The purpose of this study was to use bridge curvature method (BCM) to quantify stress, while multiscale modeling with adaptive coarsening predicted distortions based on experimentally validated models. Taguchi method and response surface method were used to optimize process parameters (energy density, hatch spacing, scanning speed and beam diameter).

Laser powder bed fusion (LPBF) offers significant design freedom but suffers from residual stresses due to rapid melting and solidification. This study presents a novel approach combining multiscale modeling and statistical optimization to minimize residual stress in SS316L.

Optimal parameters were identified through simulations and validated with experiments, achieving an 8% deviation. This approach significantly reduced printing costs compared to traditional trial-and-error methods. The analysis revealed a non-monotonic relationship between residual stress and energy density, with an initial increase followed by a decrease with increasing hatch spacing and scanning speed (both contributing to lower energy density). Additionally, beam diameter had a minimal impact compared to other energy density parameters.

This work offers a unique framework for optimizing LPBF processes by combining multiscale modeling with statistical techniques. The identified optimal parameters and insights into the individual and combined effects of energy density parameters provide valuable guidance for mitigating residual stress in SS316L, leading to improved part quality and performance.

The purpose of this study is to develop a novel comprehensive three-dimensional computational model to predict the transient thermal behavior and residual stresses resulting from the layer-by-layer deposition in the direct metal laser sintering process.

In the proposed model, time integration is performed with an implicit scheme. The equations for heat transfer are discretized by a finite volume method with thermophysical properties of the metal powder and an updated convection coefficient at each time step. The model includes convective and radiative boundary conditions for the exposed surfaces of the part and constant temperatures for the bottom surface on the build plate. The laser source is modeled as a moving radiative heat flux along the scanning pattern, while the thermal gradients are used to calculate directional and von Mises residual thermal stresses by using a quasi-steady state assumption.

In this study, four different scanning patterns are analyzed, and the transient temperature and residual thermal stress fields are evaluated from these patterns. It is found that the highest stresses occur where the laser last leaves off on its scanning pattern for each layer.

The proposed model is designed to capture the layer-by-layer deposition for a three-dimensional geometry while considering the effect of the instantaneous melting of the powder, melt pool, dynamic calculation of thermophysical properties, ease of parametrization of various process parameters and the vectorization of the code for computational efficiency. This versatile model can be used for process parameter optimization of other laser powder bed fusion additive manufacturing techniques. Furthermore, the proposed approach can be used for analyzing different scanning patterns.

This article investigates the long-run impact of remittance inflows on agricultural productivity (AGP) in emerging Asian economies (Bangladesh, Sri Lanka, Malaysia, India, Nepal, Philippines, Pakistan, and Vietnam), employing a panel dataset from 2000 to 2018.

This study initially applies cross-sectional dependence (CSD), second-generation unit root, Pedroni, and Westerlund panel co-integration techniques. Next, it uses the augmented mean group (AMG) and common correlated effect mean group (CCEMG) methods to investigate the long-term impact of remittance inflows on AGP while controlling for several other important determinants of agricultural growth, such as cultivated area, fertilizers, temperature change, credit, and labor force.

The empirical findings are as follows: The results first revealed the existence of CSD and long-term co-integration between AGP and its determinants. Second, remittance inflows significantly boosted AGP, indicating that remittance inflows played a crucial role in improving AGP. Third, global warming (changes in temperature) negatively impacts AGP. Finally, additional critical elements, for instance, cultivated area, fertilizers, credit, and labor force, positively affect AGP.

This study suggests that policymakers of emerging Asian economies should develop an exclusive remittance-receiving system and introduce remittance investment products to utilize foreign funds and mitigate agricultural production risks effectively.

This is the first empirical examination of the long-term impact of remittance flows on agricultural output in emerging Asian economies. This study utilized robust estimation methods for panel data sets, such as the Pedroni, Westerlund, AMG, and CCEMG tests.

Recognising the significance of international trade in economic growth, this research explores the drivers of exports in South Asian Association for Regional Cooperation countries from 2008 to 2021. The study employs the export demand model and the augmented exports supply model and utilises pooled time-series data. This study questions whether export supply decisions are based on traditional trade model factors, emerging trading realities or macroeconomic variables. The model based on fixed effects evaluates the connection between exports and their possible drivers. Traditional export supply models suggest determinants like production capacity, variable cost and relative pricing influencing South Asian export supply performance substantially. Changes in trade, for example, have a substantial impact on export supply, demonstrating that the trade liberalisation procedure promotes growth in exports, compression in imports and technological advancement. The worsening state of the energy industry and growing levels of corruption have proved to be significant deterrents to export supply decisions. The results verify foreign direct investment's positive and medium influence on the expansion of exports. Other variables, however, such as GDP and its growth, Official Development Assistance (ODA), development expenditure, indirect taxation, labour supply and the exchange rate of currencies, have a positive impact on the flow of exports. Furthermore, the data corroborate the notion that increased savings have a significant beneficial influence on the flow of exports. The study proposes that concerned governments examine their export policies and adopt new policies adapted in accordance with changing circumstances with the goal of increasing and enhancing the performance of exports.

The purpose of this study is to predict the antecedents of consumers' purchase intention of energy-efficient home appliances. This study incorporated two additional constructs (knowledge of eco-labels and knowledge of eco-labels) in the model of the theory of planned behavior. Consumers' enhanced trust for energy-efficient products is crucial to purchase intention. The second construct is consumers' knowledge of eco-labels. Knowledge of eco-labels would help to assess consumers' tendency toward environmental problems and intention to purchase energy-efficient home appliances.

This study used a quantitative approach using a survey (questionnaire) method to collect data of respondents. Non-probability quota based on age and convenience sampling techniques were employed to select the participants of this study.

The findings of the study revealed that consumer knowledge of eco-labels, green trust, subjective norm, attitude and perceived behavioral control have positive influence on purchase intention of energy-efficient home appliances.

Eco-labels are served as a marketing tool to distinguish producer's products from others. Prior studies have only focused on consumers' general knowledge. This study helps marketers to understand and design strategies to generate more value for green consumers. This study also contributes to the literature on green marketing by employing the theory of planned behavior.

Evaluating project success within the construction industry presents challenges due to the unique characteristics of the sector, the complexity of projects, and the involvement of diverse stakeholders. Conducting a bibliometric analysis, this paper aims to unravel the major research themes and methodologies utilised by researchers in studying the critical success criteria for construction projects, as well as extracting these success criteria.

The researchers systematically searched and screened 95 papers from Scopus and Web of Science (WoS) databases. This study conducted research focus parallelship network (RFPN) analysis and keywords co-occurrence network (KCON) analysis using BibExcel and Gephi to cluster the papers, illuminate the relationships among keywords within each cluster, and identify the primary research directions.

Using the RFPN analysis, this study classified the papers into three distinct clusters: infrastructure and public projects success, risk and knowledge management, and contractors and procurement management. Statistical techniques such as structural equation modelling (SEM) and multi-criteria decision-making methods such as analytic hierarchy process (AHP) have been used to analyse project success in the construction industry.

Considering the intensified demand for streamlined digital interactions and the increasing emphasis on sustainability and safety performance, construction companies are recommended to allocate greater investments toward the automation and digitisation of their products and processes. Prioritising modular construction and embracing transformative technologies alongside data science is crucial for enabling well-informed decision-making, and enhancing project success.

This study contributes to the existing body of knowledge by conducting a quantitative and systematic evaluation of the literature on project success criteria in the construction industry and uncovering key research areas. It addresses the pressing need to understand the complexities of construction projects amidst evolving industry dynamics and emerging disruptions. Moreover, by highlighting the implications of digital innovations and modular construction, this study urges deeper exploration into their impact on project performance and stakeholder satisfaction. This research sets a comprehensive framework for investigating the interplay between project complexity, technological advancements, and sustainable practices in the construction sector, paving the way for strategic advancements in the field.

Mergers and acquisitions (M&As) are important strategic actions undertaken by firms to access resources and markets. However, firms face substantial challenges in M&As during deal completion. While prior literature reviews synthesize the studies on the post-merger consequences of M&As, the literature on deal completion is largely fragmented. In this paper, the authors synthesize prior literature on deal completion into the antecedents and consequences framework and map various studies across the international business and management, finance and accounting literature at the macro-, meso- and micro-levels.

The authors adopt a content analysis-based methodology to conduct the review. First, the authors identify existing literature on deal completion based on keyword searches. Next, the authors propose a framework that integrates the extant literature from a multi-theoretic perspective across four broad themes: concepts, antecedents, implications and moderators. In this study, the authors consider not only empirical but also conceptual papers to strengthen the theoretical foundations of M&A literature. Finally, after synthesizing various studies, the authors highlight a future research agenda on deal completion.

Based on the review, this study provides important avenues for future research on M&A deal completion.

This study theoretically integrates multi-disciplinary and multi-country research on acquisition completion.