Journals Published by Vilnius Tech
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Establishment of housing transfer inspection items using SEM: Empirical study in Taiwan
Get PDFDisputes during the housing transfer process often lead to dissatisfaction for homeowners. This study aims to address these issues by (1) proposing a comprehensive house inspection guideline with key items and criteria, and (2) evaluating how inspection services influence homebuyers’ purchase intentions and perceived value. Based on comprehensive literature review and expert input, eight major inspection categories and 43 criteria were identified. These formed the basis for a Structural Equation Modeling (SEM) framework and three hypotheses. A pilot survey with 50 participants confirmed strong reliability (Cronbach’s Alpha: 0.880–0.945). Of 500 distributed questionnaires, 206 valid responses were collected. SEM analysis showed that inspection services significantly enhance both purchase intentions and perceived value, with R² values exceeding 79% in most areas, except for environmental inspections. Building structure, water supply/drainage and water leakage are the most influential findings in shaping perceived value. These results highlight the importance of targeted inspection services in addressing homebuyer perceived value and improving the overall housing experience in Taiwan
Sustainability, risk, and social responsibility: The new triad in real estate management
Get PDFThe increasing importance of sustainability, risk, and social responsibility in real estate management reflects evolving societal demands, regulatory pressures, and market dynamics. Motivated by the need to align real estate practices with environmental goals and social equity, this study explores how these three pillars can be systematically integrated into property management. The aim is to develop a holistic framework that transforms risks into opportunities and promotes long-term value creation. Using a mixed-methods approach, including literature analysis and semi-structured expert interviews conducted within the Romanian real estate sector, this study investigates how ESG criteria and digital technologies are currently applied in practice. Key findings reveal that tools such as BIM, digital twins, and ESG reporting enhance transparency, operational efficiency, and stakeholder engagement. The research concludes that integrating the sustainability–risk–responsibility triad provides strategic advantages, enhances resilience, and strengthens the role of real estate management in advancing sustainable development
Urban distances, individual resources, and migrant entrepreneurship: a configurational analysis in China
Get PDFGiven its significance for economic and social sustainability, migrant entrepreneurship (ME) has attracted increasing attention from scholars and policymakers. However, existing research provides limited insights into how various antecedents jointly affect ME. To address this gap, this study develops a theoretical model that integrates mixed embeddedness theory and entrepreneurial opportunity construction theory to explain the processes of opportunity construction and exploitation in ME. Using 130 cross-city migration cases in China – each comprising individuals from the same origin and destination cities – we examine how urban distances and individual resources jointly shape ME. The analysis identifies three pathways to high ME: the opportunity-resource endowed path, the resource bricolage path, and the opportunity-resource matching path. Although no single factor is necessary for high ME, greater geographic distance consistently promotes it. This study advances our understanding of the interplay between urban conditions, individual resources, and ME, and further enriches the mixed embeddedness theory by integrating the opportunity construction perspective
Barriers to blockchain implementation in supply chain finance – based on perspectives of grey transaction behaviour
Get PDFThe purpose of this study is to identify grey transaction behaviours that function as barriers to blockchain implementation in supply chain finance, explore which obstructions arise from these grey transaction behaviours and subsequently hinder blockchain implementation, and further measure the influence levels of the corresponding implementation barriers. The Delphi method and DANP are the main analysis methods used in this study. According to the analysis, three grey transaction behaviours that function as implementation barriers are identified, namely, kickbacks, internal and external accounting, and informal transaction relationships with banks. In addition, seven obstructions arise from these three barriers, and these obstructions can be adopted to explain why the three barriers hinder blockchain implementation. Finally, although internal and external accounting represents the main barrier, informal transaction relationships with banks may function as a critical underlying barrier that enhances the other two barriers. This study contributes in that it fills the gap of existing studies related to the intersection of supply chain finance and blockchain and provides a new perspective enabling practitioners to rethink how blockchain can be successfully implemented in the context of supply chain finance
Unveiling leadership dynamics and tournament incentives: insights from the environmental misconduct of Chinese listed companies
Get PDFThis study investigates whether the tournament compensation motivate executives to adopt environmentally responsible practices and reduce environmental violations. Focusing on CEO characteristics, we also examine whether politically connected CEOs and CEO gender enhance the effectiveness of these incentives in mitigating corporate environmental violations. Using a fixed-effects model, two stage least square (2-SLS) and the Generalized Method of Moments (GMM) on data from Chinese companies, spanning from 2010 to 2023, this study finds that executive tournament incentives play a significant role in reducing environmental violations. Our results further reveal that political connections and female CEOs strengthen the negative relationship between tournament incentives and environmental violations, demonstrating the critical influence of leadership diversity and institutional ties in driving corporate sustainability. Additionally, our findings provide robust support for tournament theory, highlighting the pivotal role of CEOs in shaping corporate environmental behaviour. This study provides pertinent insights for regulators and policymakers, assisting them to develop tailored strategies, regulations and legislative frameworks to reduce environmental violations and improve sustainable development
What causes the return and volatility spillover in Chinese green finance markets? A time-frequency perspective
Get PDFThis paper analyzes both return and volatility spillovers between green bonds, green stocks, clean energy, and carbon markets from April 28, 2014, to May 31, 2024, using the time-frequency connectedness methodology. Further, determinants of these spillovers are examined from perspectives of economic fundamentals (macroeconomics, inflation, and rate), market contagion (market volatility and investor sentiment), and uncertainties (EPU, CPU, GPR, and the COVID-19 pandemic) with the linear regression and quantile regression methods. Our investigation demonstrates that return and volatility spillovers exhibit significant crisis jumps during periods of financial turmoil. During most periods, both return and volatility spillovers occur predominantly in the short run. Second, green bonds and carbon markets show safe-haven characteristics as net risk recipients. Furthermore, economic fundamentals, market contagion, and uncertainty factors exhibit obvious impacts on both green finance market spillovers, albeit in differing magnitudes and directions. Notably, both return and volatility spillovers in the short and long run are determined by economic fundamentals, market contagion, and uncertainty variables. What’s more, these factors exhibit stronger interpretations of extreme return spillovers. These findings pose significant ramifications for risk mitigation and portfolio diversification for investors and authorities throughout Chinese green finance markets
Emergency risk assessment and rescue of groundwater pollution caused by mining based on 5G network
Get PDFRecently, heavy metal pollution of soil, land degradation and groundwater pollution generated due to the lack of supporting environmental protection measure, which not only endanger human health, but also affect the sustainable development of mining industry. In order to avoid groundwater pollution, and improve risk assessment and rescue levels of underground water pollution, and a wireless remote emergency risk assessment and rescue system based on 5G core network is constructed, and emergency risk assessment model of groundwater pollution caused by mining is designed. The types of ground water pollution are analyzed. The emergency risk assessment of groundwater pollution is carried out, and the evaluation method of groundwater pollution is designed. A data mining algorithm is designed for optimizing 5G communication network. Taking groundwater in a mining area as the research object, 15 heavy metal indexes of 12 monitoring wells in the mining area were monitored and analyzed. The results show that proposed emergency risk evaluation and rescue of groundwater pollution caused by mining based on 5G network has better communication performance, indexes of as, Sb, Co, Fe and Mn in underground water exceed the standard. And then 5G core network construction of groundwater pollution emergency risk assessment and rescue concludes user plane function/mobile edge computing (UPF/MEC) sink and independent private network is achieved. A case study is carried out using a coal mine as researching object, results showed that proposed remote emergency risk assessment and rescue of groundwater pollution has less time delay and packet loss rate, therefore proposed emergency risk assessment and rescue of groundwater pollution has better communication performance. Finally, the emergency rescue countermeasures of groundwater pollution are taken according to evaluation results
The impact of lakeshore modifications and constructions on visual landscape quality: a mixed methods study
Get PDFLakeshore areas continue to be threatened by increasing human activities and land use. Development and large construction projects in lakeshore areas affect both the lake’s ecological condition and its landscape quality and aesthetics. To minimize and prevent the occurrence of significant visual impacts, it is important to understand and evaluate the magnitude of damage and the factors contributing to such impacts from development activities. In this study, a mixed methods approach is used to assess the visual impact of modifications and constructions on the lakeshore landscape. This includes (1) an objective landscape indicator-based assessment method to measure the extent of construction and modification impacts on the visual landscape, and (2) a visual perception-based assessment method to capture receptors’ evaluations of the visual landscape changes and visual impact factors on the lakeshore. Integrating the results from both methods yields a comprehensive assessment of visual impact. The results of both assessment methods indicate that the visual quality of the lakeshore landscape declined significantly during the construction phase. In addition, this study concludes that this mixed approach to visual impact assessment has greater advantages than a single approach and provides more dimensional information, criteria, and perspectives
Motivation to pangolin conservation among Gen Z: applying the extended Theory of Planned Behavior
Get PDFThe study explores the factors influencing pangolin conservation intentions among Gen Z using the extended Theory of Planned Behavior (TPB). Despite ongoing conservation efforts, pangolins remain one of the most trafficked animals globally, facing severe threats from illegal wildlife trade. To address this, the research investigates the roles of attitudes, subjective norms, and perceived behavioral control, along with knowledge and experience, in shaping conservation behaviors. A quantitative approach was employed, utilizing a structured questionnaire to gather data from 377 respondents. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied for data analysis.
The results show that subjective norms and perceived behavioral control are the most significant predictors of behavioral intentions, whereas attitudes, knowledge, and past experiences related to wildlife conservation or pangolins have a relatively weak influence. While they still have a positive effect, these factors do not influence the intention to engage in pangolin conservation behavior as strongly. These findings suggest that conservation campaigns should focus on leveraging social influences and enhancing individuals’ perceived control over their actions rather than solely increasing factual knowledge
A novel hybrid model for predicting the bearing capacity of piles
Get PDFDue to the uncertainty of soil condition and pile design characteristics, it is always a challenge for geotechnical engineers to accurately determine the bearing capacity of piles. The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. The improved PSO algorithm was used to optimize the LSSVM hyperparameters. The performance of the IPSO-LSSVM model was compared with seven artificial intelligence models, namely adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5MT), multivariate adaptive regression splines (MARS), gene expression programming (GEP), random forest (RF), regression tree (RT) and a stacked ensemble model. Six statistical indices (e.g., coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), relative root mean squared error (RRMSE), BIAS and discrepancy ratio (DR)) were used to evaluate the performance of the models. The R2, MAE, RMSE, RRMSE and BIAS values of the IPSO-LSSVM model were 1, 4.27 kN, 6.164 kN, 0.005 and 0, respectively, for the training datasets and 0.9977, 22 kN, 36.03 kN, 0.0275 and –11, respectively, for the testing datasets. Compared with the ANFIS, MARS, GEP, M5MT, RF, RT and the stacked ensemble models, the proposed IPSO-LSSVM model shows high accuracy and robustness on the test datasets. In addition, the sensitivity, uncertainty, reliability and resilience of the IPSO-LSSVM model were also analyzed in this study.
First published online 22 October 202