Search Results (33)

The current real estate market is in disarray, implying that reforms and incorporation of sustainable and green elements are crucial, especially for younger generations. This becomes more vivid for the case of developing countries and the Middle East as there is a growing interest in green living concepts. This study focuses on Turkish young adults and their purchasing intentions of green real estate options in line with the sustainability agenda for Turkish development goals. In this sense, the indirect impacts of greenwashing and perceived value are examined to address the underlying determinants of purchasing intentions. The theoretical setting of the research combines the stimulus–organism–response model and the theory of planned behavior. Through combined purposive and convenience sampling methods and using partial least squares structural equation modeling (PLS-SEM), a total of 203 surveys were analyzed. The results highlight that a well-established green marketing campaign can uplift perceived value, which in turn enhances purchasing intentions during the evaluation process among potential buyers. Greenwashing is a major diminisher for consumers’ intentions as it creates doubt, distrust, and negative emotions, thus creating a mental barrier for forming intentions towards purchasing green housing options. The findings of this research provide both theoretical and practical implications for improving housing options for young adults through empirical analysis of marketing and consumer behavior mechanisms. Full article

This study investigates the determinants of turnover intention among real estate sales professionals in Lebanon and the United Arab Emirates (UAE), two markets that represent contrasting economic realities within the MENA region. Drawing on Herzberg’s Two-Factor Theory, Vroom’s Expectancy Theory, and March and Simon’s Push-Pull Model, this study adopts a multidimensional framework to assess the effects of compensation, job stress, career growth opportunities, and work–life balance on employee retention. A quantitative method was employed using a structured questionnaire administered to 832 respondents (425 in the UAE and 407 in Lebanon), and data were analyzed using Structural Equation Modeling. The results reveal that job stress is the most influential predictor of turnover intention, particularly in Lebanon, followed by work–life balance, compensation, and career growth opportunities. These findings underscore the importance of psychological well-being and structural incentives in talent retention. By offering empirical evidence from an underexplored regional labor market, the study contributes to the global turnover discourse and provides comparative insights into the labor dynamics of both a crisis-prone and a high-growth economy. The results carry significant practical implications for HR managers, firm owners, and policymakers, highlighting the necessity of adopting holistic and context-sensitive retention strategies that extend beyond financial rewards to include flexible work arrangements, career development frameworks, and supportive workplace cultures. Full article

Radiata pine (Pinus radiata D. Don) is New Zealand’s dominant plantation species, supporting carbon sequestration under the national Emissions Trading Scheme. However, existing stand-level carbon models cannot estimate individual tree carbon stocks which are often required for modern remote sensing-based forest inventories. This study developed comprehensive allometric equations for predicting tree-level carbon in radiata pine using an extensive dataset of 894 trees spanning ages 1–42 years across eight New Zealand locations. We fitted 12 models predicting stem wood, bark, branch, and foliage biomass from varying combinations of tree height, diameter at breast height, stand age, stand density and wood density. Models incorporating both height and diameter achieved excellent accuracy for stem wood and bark (R² > 0.99, log-transformed scale), while inclusion of age, stand density and wood density substantially improved crown component predictions (R² = 0.95 for branches and 0.93 for foliage). Biomass predictions were converted to carbon using component-specific and age-dependent carbon fractions derived from New Zealand radiata pine, avoiding biases from generic conversion factors. The resulting equations provide a tiered system accommodating different data availability levels and are directly compatible with LiDAR-derived tree attributes. These models provide a robust framework for accurate individual-tree carbon estimation, supporting both operational plantation management and robust carbon accounting across New Zealand’s radiata pine estate. Full article

This study develops a weather-driven bio-economic optimization framework for agricultural planning in Guyana by integrating weather simulation, crop modeling, and multi-objective optimization. Precipitation was modeled using a first-order Markov chain with fitted distribution, while temperature and relative humidity were simulated using stochastic differential equations. Reference evapotranspiration was estimated using an artificial neural network. These simulated weather variables were then used as inputs to AquaCrop to estimate rice, maize, and soybean yields across multiple planting intervals. A multi-objective optimization model was then applied to optimize gross profit, economic water productivity, and land use efficiency. Validation at the Rose Hall Estate showed strong accuracy for rice and maize (MAPE < 10%) and moderate accuracy for soybeans. Scenario analyses for the 2024–2025 season, assuming 25% and 50% export targets, revealed that rice–maize double cropping produced the highest profitability, while soybean–maize combinations were less favorable. The framework replaces static yield assumptions with dynamic, simulation-driven models that incorporate price forecasts and allow substitution of alternative forecasting or crop simulators to enhance precision. The scenario-based design provides a flexible decision-support platform for optimizing crop selection, planting intervals, and resource allocation under climate variability and market uncertainty. Moreover, the framework is scalable and well-suited for evidence-based agricultural planning. Full article

With growing competition in today’s business environments, organizations rely on Performance Measurement Systems (PMSs) to address challenges, assess progress, and enhance performance. While PMSs have been extensively researched, their impact on organizational performance remains a topic of debate, particularly in developing countries like Jordan, where relevant research is scarce. This study investigates the effects of Comprehensive PMSs (CPMSs) on organizational effectiveness and the financial and non-financial performance of the companies in the Jordanian Industrial Estates. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), this study analyzes empirical data to test proposed hypotheses regarding the impacts of these systems on organizations’ performance. The findings confirm that CPMSs significantly and positively influence overall organizational performance, organizational effectiveness, and both financial and non-financial performance. These results highlight the critical role of CPMSs in improving organizational outcomes, supporting their adoption for strategic decision-making. However, given the limited research on PMSs in developing economies, further studies are needed to explore contextual influences and long-term implications. Full article

Modeling risk evolution in financial networks presents both practical and theoretical challenges, particularly during periods of heightened systemic stress. This issue has gained urgency recently in China as it faces unprecedented financial strain, largely driven by structural shifts in the real estate sector and broader economic vulnerabilities. In this study, we combine Fractional-order Partial Differential Equations (FoPDEs) with network-based analysis methods, proposing a hybrid framework for capturing and modeling systemic financial risk, which is quantified using the $\Delta CoVaR$ algorithm. The FoPDEs model is formulated based on reaction–diffusion equations and discretized using the Caputo fractional derivative. Parameter estimation is conducted through a composite optimization strategy, and numerical simulations are carried out to investigate the underlying mechanisms and dynamic behavior encoded in the equations. For empirical evaluation, we utilize data from China’s financial and real estate sectors. The results demonstrate that our model achieves a Mean Relative Accuracy (MRA) of 95.5% for daily-frequency data, outperforming LSTM and XGBoost under the same conditions. For weekly-frequency data, the model attains an MRA of 91.7%, exceeding XGBoost’s performance of 90.25%. Further analysis of parameter dynamics and event studies reveals that the fractional-order parameter $\alpha$, which controls the memory effect of the model, tends to remain low when $\Delta CoVaR$ exhibits sudden surges. This suggests that the model assigns greater importance to past data during periods of financial shocks, capturing the persistence of risk dynamics more effectively. Full article

This study explores the role of egoistic and altruistic values on green real estate purchase intention among young consumers in Canada aged between 20 and 40 years. In addition, this study examines the mediating effects of pro-environmental self-identity between social consumption motivation and green real estate purchase intention. A quantitative cross-sectional research design with an explanatory nature is employed. A total of 432 participating consumers in Canada, comprising 44% men and 48% women, with a graduate educational background accounting for 46.7%, and the ages between 24 and 35 contributing 75.2%, were part of the study, and the data collection used a survey method with a purposive sampling, followed by a respondent-driven method. Descriptive and inferential statistics were performed on the scales used for the study variables. A structural equational model and path analysis were conducted to derive the results, and the relationships were positive and significant. The study results infer the factors contributing to green real estate purchase intention, including altruistic value, egoistic value, social consumption motivation, and pro-environmental self-identity, with pro-environmental self-identity mediating the relationship. This study emphasizes the relevance of consumer values in real estate purchasing decisions, urging developers and marketers to prioritize ethical ideas, sustainable practices, and building a feeling of belonging and social connectedness. Offering eco-friendly amenities and green construction methods might attract clients, but creating a secure area for social interaction is critical. To the best of the authors’ knowledge, this research is the first to explore the role of egoistic and altruistic values on purchase intention, mainly in the housing and real estate sector, with the target consumers being young consumers in Canada. Full article

The aim of this study was to analyze innovation acceptance in condominiums using structural equation modeling (SEM) based on latent variables derived from a second-order confirmatory factor analysis (CFA). The authors focused on four groups of latent variables, namely, the characteristics of innovation adopters (CHARACTER), desired innovation categories (CATEGORY), trust in developers (TRUST), and innovation adoption (ADOPTION), collected from an intensive literature review. Data were gathered from 400 residents of high-rise condominiums across twenty-one central districts of Bangkok via purposive sampling. The analysis revealed that only the desired innovation categories had a direct effect on innovation adoption. In contrast, the characteristics of buyers and trust in developers did not have direct effects on innovation adoption but exerted indirect effects through the desired innovation categories. The findings illustrate how SEM can be applied to study the adoption of innovation by real estate buyers. In practical applications, project developers and designers should study which innovations are desired by buyers. This involves considering the buyers’ characteristics and level of trust in the developers. Such an analysis would enable them to design projects that maximize their responsiveness to buyers’ needs and would not impose excessive cost burdens as well as enhancing income opportunities and achieving sustainable competitive advantages. Full article

In the post-digital era, virtual reality (VR) technology is increasingly being utilized in the real estate industry. In this study, the influence of functional experience with VR technology (e.g., interactivity and flexibility) on consumers’ offline house viewing intentions is explored. On the basis of efficiency–flexibility ambidexterity and customer inspiration theory, a structural equation model was employed to analyze empirical data collected from 388 consumers in the Guangdong–Hong Kong–Macao Greater Bay Area. The key findings are as follows: (1) VR technology features have significant positive effects on customer inspiration, which in turn enhances customers’ willingness to view houses offline; (2) VR presence, enjoyment, interactivity, and flexibility all contribute to customer inspiration, with VR presence having the most substantial impact; and (3) VR knowledge and consumer demand for uniqueness significantly moderate the relationship between VR technology features and customer inspiration. For example, consumers with substantial VR knowledge can more effectively leverage VR technology, whereas those with a strong need for uniqueness are more likely to be inspired by the innovative aspects of VR. This research provides theoretical support for the application of VR technology in real estate marketing and practical guidance for enterprises to optimize VR marketing strategies, improve consumer experiences, and drive offline transactions. These insights can help companies better understand consumer psychology and behaviour in the digital marketing landscape. Full article

The construction sector holds a paramount position in the economic landscape of any country, serving as its foundational pillar. This sector, characterized by its diverse and dynamic environment, is crucial in job creation across various domains, including transportation, real estate, manufacturing, trade, warehousing, wholesale, and leasing services. Employing about one quarter of the global workforce, its significance is undeniable. Despite its pivotal role, the construction sector grapples with significant mental health and social sustainability challenges. Reports in recent years indicate that approximately one in four individuals worldwide experiences various forms of mental disorders. A study by the Global Burden of Disease in 2010 revealed that around 400 million people globally suffer from depression with projections suggesting that depression could be the leading cause of employee mortality by 2030. This underscores the critical need to address mental health and well-being issues in this sector. While the existing literature has presented numerous studies and reliable scales linking employee mental health and well-being to factors such as job satisfaction, productivity, absenteeism, and low turnover rates, these studies often operate in isolation, concentrating on specific aspects of mental health. This study views mental health and well-being as essential parts of defining social sustainability as a comprehensive concept. Moreover, limited research has been conducted to assist organizations in decision making and facilitate efforts to enhance the social sustainability of employees in the construction sector, highlighting a noticeable research gap. To address this gap, our study adopted a comprehensive mixed-methods approach, incorporating semi-structured interviews, surveys, and structural equation modeling to identify the underpinning criteria that define the social sustainability of employees. This study accordingly incorporated the identified criteria to evaluate the relationship and impact of these factors on employees’ job satisfaction, ultimately contributing to the assurance of social sustainability for employees within the construction sector in the UAE. This holistic approach seeks to establish the intricate relationship between employees’ job satisfaction and their mental health, providing valuable insights for guiding organizational decisions and fostering improvements in employee social sustainability in the construction sector generally and the UAE construction sector in particular. Full article

China’s current real estate market transactions are relatively subdued; hence, finding means to repower the engine for further development becomes vital. However, few studies investigated the relationships between family non-economic factors, assets, and housing debt. This study highlights the impact of family members’ health, insurance, and social capital’s impact on housing debt and assets. The family-size data from the China House Finance Survey are analyzed using a partial least squares structural equation model. The results indicate that family members’ poor health and uninsured endowment insurance individuals negatively affect housing debt and family assets. In contrast, the impact of medical insurance is insignificant. Besides, social capital substantially and positively impacts assets and debts. The labor supply and the proportion of kids have a negligible impact. Hence, this study recommends that loan-offering enterprises may change their marketing targets according to family situations, such as health status and insurance coverage. The government might promote endowment insurance and strengthen higher education to revitalize the real estate industry. Full article

Residential satisfaction has been heavily discussed worldwide amid the increasing emphasis on the social dimension of sustainability. However, consensus has not been reached regarding its determinants, and little research has focused on the subsidized housing estates (SHEs) in post-reform China. Using data collected from field observations in Guangzhou, this study developed a conceptual framework on the basis of the ACSI and Campbell’s models and employed structural equation modeling to investigate what and how factors pertaining to both the built and social environments influenced residential satisfaction of SHEs. Generally, SHE residents were moderately satisfied with their housing estates, although challenges persisted in estate management and security, suggesting that the performance of China’s subsidized housing policies were acceptable. Echoing existing arguments, this empirical investigation revealed the substantial positive roles played by both the built and social environments in shaping residential satisfaction. Specifically, the livable built environment characterized by large housing size and well-equipped neighborhoods, coupled with the friendly social environment marked by intimate neighborly relationships and diverse community activities, correlated with an elevated level of residential satisfaction of SHEs. Importantly, residents’ subjective perceptions of the built environment emerged as the most influential factor, which acted a significant mediating role, linking both objective attributes and individual expectations to residential satisfaction. This underscored the necessity of integrating public opinions into the planning process to meet SHE residents’ actual desires. These findings not only extended the scholarly discourse on residential satisfaction, especially among disadvantaged groups in low-income housing estates, but also advanced urban sustainable development by providing references for enhancing SHE performance. Full article

Fugitive methane emissions from the mining industry, particularly so-called ventilation air methane (VAM) emissions, are considered among the largest sources of greenhouse gas (GHG) emissions. VAM emissions not only contribute to the global warming but also pose a significant hazard to mining safety due to the risk of accidental fires and explosions. This research presents a novel approach that investigates the capture of CH₄ in a controlled environment using 1-butyl-3-methylimidazolium bis (trifluoromethylsulfonyl) imide [BMIM][TF₂N] ionic liquid (IL), which is an environmentally friendly solvent. The experimental and modelling results confirm that CH₄ absorption in [BMIM][TF2N], in a packed column, can be a promising technique for capturing CH₄ from point sources, particularly the outlet streams of ventilation shafts in underground coal mines, which typically accounts for <1% v/v of the flow. This study assessed the effectiveness of CH₄ removal in a packed bed column by testing various factors such as absorption temperature, liquid and gas flow rates, flow pattern, packing size, desorption temperature, and desorption pressure. According to the optimisation results, the following parameters can be used to achieve a CH₄ removal efficiency of 23.8%: a gas flow rate of 0.1 L/min, a liquid flow rate of 0.5 L/min, a packing diameter of 6 mm, and absorption and desorption temperatures of 303 K and 403.15 K, respectively. Additionally, the experimental results indicated that ILs could concentrate CH₄ in the simulated VAM stream by approximately 4 fold. It is important to note that the efficiency of CH₄ removal was determined to be 3.5-fold higher compared to that of N₂. Consequently, even though the VAM stream primarily contains N₂, the IL used in the same stream shows a notably superior capacity for removing CH₄ compared to N₂. Furthermore, CH₄ absorption with [BMIM][TF₂N] is based on physical interactions, leading to reduced energy requirements for regeneration. These findings validate the method’s effectiveness in mitigating CH₄ emissions within the mining sector and enabling the concentration of VAM through a secure and energy-efficient procedure. Full article

China officially entered the aging society in 2000, witnessing a rapid surge in demand for senior care services. In response, the real estate industry introduced the concept of Continuing Care Retirement Communities (CCRCs). The relationship between this model and complex risks in urban security resilience encompasses various factors, including filial care, demand for senior care, and urban sustainable development. The strategic layout and planning of CCRC enhance the security resilience of urban operational systems in the face of intricate senior care risks. However, the development and operation of CCRCs have encountered the challenge of sluggish project progress, primarily due to a lack of robust purchase intention. This study investigates the factors influencing the purchase intention of CCRCs in mainland China, using the Theory of Planned Behavior (TPB) as the foundational theoretical model. Additionally, three contextual constructs (economic cost, product performance, and external stimuli) were introduced to form the initial model. Based on the initial model, six factors were identified and nine hypotheses were proposed. A questionnaire survey was conducted to collect data, and Structural Equation Modeling (SEM) analysis was employed to test the proposed hypotheses. The results indicate that consumers’ purchase intention of CCRCs is primarily influenced by product performance and subjective norms, followed by economic cost. At the same time, external stimuli have a significant indirect effect on it. Full article

In contrast to the extensive research on the impact of green buildings on character, property values, energy efficiency, and environmental quality, less emphasis has been placed on understanding the factors driving users’ selection of green real estate projects, an essential element for the future business sustainability of such buildings. This study investigates the connection between various user motivations, such as perceived usefulness, perceived usability, a sense of responsibility, awareness of consequences, and perceived behavioral control, and their impact on Asian users’ willingness to pay for green real estate projects. These users include those who have already invested in and are considering long-term investment in such projects. An empirical methodology, integrating these motivations with a technology acceptance model, is proposed to assess their willingness to pay either through renting or buying. The study applies structural equation modeling to analyze sample data against a hypothetical model, thereby testing the hypotheses. It concludes that, in influencing green real estate projects, the following apply: (1) perceived usefulness and perceived behavioral control significantly shape Asian users’ attitudes towards financial commitment; (2) perceived usability directly influences their perceived usefulness; (3) awareness of consequences acts as a mediating factor in the relationship between perceived usability and behavioral control among these users. Moreover, the study offers strategic design recommendations for future green real estate projects, informed by the diverse motivational factors of Asian users, to guide their purchasing and selection decisions. Full article