Empirical Analysis Research Articles

Empirical Analysis of Critical Success Factors for Mechatronics Implementation in Architecture, Engineering, Construction and Operations Projects in Nigeria

The Fourth Industrial Revolution (4IR) has ushered in a new era of technological advancements that are transforming industries worldwide. One such technology that is revolutionizing the construction industry is mechatronics, which has the propensity to enhance the operations, activities, productivity and efficiency of the sector’s activities. Despite the numerous advantages of mechatronic technologies, their successful implementation in the context of developing countries poses unique challenges and considerations. Therefore, this study seeks to identify and evaluate the critical success factors (CSFs) for mechatronics implementation in architecture, engineering, construction and operations (AECO) projects. Existing CSFs were extracted from extant studies, which helped formulate the questionnaire disseminated to 372 construction professionals in Nigeria, including architects, builders, quantity surveyors, and engineers (mechanical, civil, electrical). The methodology also employed exploratory factor analysis (EFA), which facilitated the identification of key themes within the data. Through this application, six clusters of CSFs were revealed: organizational factors, financial considerations, technological aspects, collaboration and knowledge sharing, regulatory and policy factors, and sustainability and environmental considerations. From a theoretical perspective, the identified clusters of critical success factors provide a comprehensive framework that encompasses various dimensions of successful mechatronics adoption in the Nigerian construction industry. This study advances scientific knowledge on CSFs for the adoption of mechatronic technologies in the Nigerian construction industry, providing a comprehensive understanding of the factors that drive successful implementation. For policymakers, this study’s findings will be invaluable in shaping supportive policies and strategies that foster the widespread adoption of mechatronics in the construction sector.

A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions

This study compares the effectiveness of different methods for coal thickness identification, aiming to identify the most accurate approach and provide a reference for intelligent coalmine development. Focused on the No. 2 coal seam in a mining area in Shanxi, China, the analysis employs well log-constrained impedance inversion and seismic multi-attribute techniques. The results show that the back propagation (BP) neural network model, as part of the seismic multi-attribute approach, delivers prediction accuracy comparable to the well log-constrained inversion method. Specifically, after applying proper static corrections, a four-layer BP neural network was constructed using four optimized sensitive attributes as the input layer, achieving an error range of 0.11% to 1.36%, compared to 0.03% to 6.59% for the logging-based method. The BP neural network demonstrated strong applicability in complex geological environments. Empirical analysis further validated the BP neural network’s geological reliability and practicality in systematic coal thickness determination.

Empowered Minipublics for Democratic Renewal? Evidence from Three Conjoint Experiments in the United States, Ireland, and Finland

This article investigates the potential of deliberative minipublics to provide a new set of institutions for democratic renewal. Using three preregistered and identical conjoint experiments in the United States, Ireland, and Finland, it first shows that minipublics are moderately attractive institutional innovations, but that in all three country contexts, citizens in general are very reluctant to grant them empowerment and autonomy as well as ask for additional provisions (such as large size or large majorities for recommendations). Subgroup analyses, however, reveal that especially participation in minipublics as well as trust in other citizens as decision-makers in combination with low political trust produces more support for empowered and autonomous minipublics. But what stands out in the empirical analysis is that most citizens want minipublics as additions to the representative system, not as a replacement of the existing democratic infrastructure, as some minipublic advocates have suggested.

Predicting Natural Gas Production in Various Nations Using a Fractional Grey Bernoulli Approach

Accurate forecasting of natural gas production is crucial for economic stability, environmental sustainability, and market investment. This study presents an advanced forecasting method using the fractional grey Bernoulli model, which combines fractional accumulation and Bernoulli processes to enhance the predictive accuracy for nonlinear datasets. The model’s versatility and flexibility allow it to adapt to various data characteristics and complexities, thereby outperforming traditional grey models in forecasting performance. To optimize the model parameters, this study employs the Particle Swarm Optimization (PSO) algorithm, further improving the model’s effectiveness. Empirical analysis of natural gas production data from Brazil, Italy, and Qatar demonstrates that the model exhibits significant advantages in both fitting and forecasting capabilities. The findings indicate that the fractional grey Bernoulli model achieves high accuracy and reliability in predicting natural gas production in these countries, providing a robust framework for strategic energy planning and investment decision-making. With average forecast errors of 1.9113%, 4.0353%, and 1.8902% for natural gas production in Brazil, Italy, and Qatar respectively, this study underscores the model’s effectiveness in enhancing forecast reliability and minimizing risk, providing valuable insights for sustainable energy development.

Measuring complexity in mega construction projects: fuzzy comprehensive evaluation and grey relational analysis

PurposeMega construction projects (MCPs), characterized by their vast scale, numerous stakeholders and complex management, often face significant uncertainties and challenges. While existing research has explored the complexity of MCPs, it predominantly focuses on qualitative analysis and lacks systematic quantitative measurement methods. Therefore, this study aims to construct a complexity measurement model for MCPs using fuzzy comprehensive evaluation and grey relational analysis.Design/methodology/approachThis study first constructs a complexity measurement framework through a systematic literature review, covering six dimensions of technical complexity, organizational complexity, goal complexity, environmental complexity, cultural complexity and information complexity and comprising 30 influencing factors. Secondly, a fuzzy evaluation matrix for complexity is constructed using a generalized bell-shaped membership function to effectively handle the fuzziness and uncertainty in the assessment. Subsequently, grey relational analysis is used to calculate the relational degree of each complexity factor, identifying their weights in the overall complexity. Finally, the weighted comprehensive evaluation results of project complexity are derived by combining the fuzzy evaluation results with the grey relational degrees.FindingsTo validate the model’s effectiveness, the 2020 Xi’an Silk Road International Conference Center construction project is used as a case study. The results indicate that the overall complexity level of the project is moderate, with goal complexity being the highest, followed by organizational complexity, environmental complexity, technical complexity, cultural complexity and informational complexity. The empirical analysis demonstrates that the model can accurately reflect the variations across different dimensions of MCP complexity and can be effectively applied in real-world projects.Originality/valueThis study systematically integrates research on MCPs complexity, establishing a multidimensional complexity measurement framework that addresses the limitations of previous studies focusing on partial dimensions. Moreover, the proposed quantitative measurement model combines fuzzy comprehensive evaluation and grey relational analysis, enhancing the accuracy and objectivity of complexity measurement while minimizing subjective bias. Lastly, the model has broad applicability and can be used in MCPs across different countries and regions, providing a scientific and effective basis for identifying and managing MCP complexity.

Cartographies of Emergence of Latin American Communities and British Latinx Imaginaries in London

This paper explores Latin American communities in London through the conceptual lens of cartographies of emergence in translocational perspective suggesting that it provides a less binary approach than the more commonly used perspective of ‘(in)visibility’. This is informed by the empirical analysis of Latin American community‐building through their negotiation of precarity and collective resistance. It also reflects on the contested nomenclatures used to denote the ‘community’ such as Latin American and British Latinx as well as plural interpretations of British Latinidades and British Latinx imaginaries, the latter to capture the mutable and dynamic identities and belonging.

Unlocking the role of biofuel energy consumption and democratic accountability on ecological sustainability in the United States: a quantile-based analysis

ABSTRACT Energy is vital for economic growth, but it concurrently generates CO2, the primary driver of climate change and environmental degradation. This study investigates the impact of biofuel energy consumption on CO2 emissions in the United States. The study also considers the role of government stability, renewable energy, and democratic accountability. Utilizing data from 1990 to 2019, the empirical analysis employs advanced quantile-based methodologies, providing an all-inclusive insight into the interrelationships at various quantiles of the data distribution. The results from the analysis highlight that government stability, democratic accountability, economic growth, and biofuel energy consumption collectively contribute to environmental sustainability in the United States. Lastly, all the variables can significantly predict CO2 emissions across all quantiles. Based on these results, policies were formulated.

Relationship lending and the role of loan officers in China: Empirical analysis based on the data on companies in industrial clusters

Relationship lending refers to lending behavior in which a financial institution makes loan decisions based on the accumulated soft information on the borrower. Empirical studies have revealed the positive effect of relationship lending. However, questions concerning who generates and accumulates soft information and in what ways in China have never been analyzed empirically. This paper empirically aims to explain whether loan officers generate soft information in relationship lending using data collected through surveys conducted among SMEs in the apparel industry clusters in Zhejiang and Jiangsu provinces. Based on the results of this study, relationship banking in China, which deals with the role of loan officers, seems even more important than is generally recognized.

Technological intensity and innovation capability: evidence from an emerging country

Purpose In general, it is believed that firms belonging to the high technological intensity stratum have a more innovation capability. However, evidence has begun to appear in the literature demonstrating that firms in the low-tech stratum also have the innovation capability. This paper seeks to solve this dilemma. Through an analysis of industrial firms in an emerging country, this study aims to identify the existence of innovation capability in all strata of technological intensity. Design/methodology/approach The authors empirically assessed the impact of innovation capability on the performance of firms in all strata of technological intensity. The authors studied a sample of 910 firms from different industries and technology intensities operating in Peru, by applying a partial least squares structural equation model (PLS-SEM). Findings This study obtained three important findings. First, the authors show that innovation capability is present in all strata of technological intensity. Second, innovation capability differs according to the technological stratum. Third, firms in the lowest technological stratum can use more capabilities than those in the other strata. Originality/value This study pioneers the empirical analysis of innovation capability and technological intensity simultaneously to verify that innovation capability exists in the four strata of technological intensity.

The polarisation of Italian metropolitan areas, 2000–2018: structural change, technology and growth

Abstract Large cities are a key driver of technological innovation and economic growth. This paper investigates the developments of Italian metropolitan areas, building on insights from economic geography and innovation studies. The key questions to be investigated are the following: (i) Which trajectories of population and economic change can be identified for Italian metropolitan areas? Are we facing a process of economic and technological polarisation that may worsen the country’s imbalances? (ii) What is the role played in such developments by technological and structural change, and in particular by digital technologies and the rise of finance? The empirical analysis investigates the patterns of technological and economic indicators for the period 2000–2018 for 14 Italian metropolitan areas, proxied by their provinces, providing evidence of growing polarisation between Milan, where most positive developments are concentrated, and the other metropolitan zones. Rome has been losing ground in most fields; Venice and Genoa are characterised by industrial decline. Few mid-sized cities show some economic dynamism—including Bologna and Cagliari—while most southern and insular Italian cities increase their gap relative to the performances of leading metropolitan areas.

Impact of the Belt and Road Initiative on Global Trade and Economic Growth Amid China’s Slowing Growth

Since the inception of the Belt and Road Initiative (BRI), China’s trade relations with participating nations have experienced substantial growth. This study utilises data from the World Trade Organization (WTO) covering the years 2003 to 2021 to explore the dynamics of China’s foreign trade and assess the impact of the BRI on trade interactions with China. One of the central objectives of our research is to determine whether participation in the BRI has significantly contributed to trade growth with China, thereby strengthening economic ties and fostering bilateral cooperation. Another important objective is to analyse the changes in the BRI’s role in driving global economic growth amid China’s slowing growth. Through empirical analysis using data from over 180 countries, we evaluate the heterogeneous effects of the BRI across different continents. Our findings indicate that the BRI has had the most pronounced trade-stimulating impact on the continents of America, Europe and Africa, while trade volumes with Asian countries have seen only marginal increases. The relatively smaller trade growth in Asian countries can be attributed to the already high volume of trade activities due to their geographical proximity to China, which diminishes the marginal impact of the BRI. Additionally, the significant infrastructure investments under the BRI have been predominantly directed towards Africa and Europe, further explaining the smaller trade-enhancing effects observed in Asia compared to other continents. Furthermore, we identified the spillover effects of the BRI on non-participating countries. Moreover, significant differences in the BRI’s impact on global economic growth were observed before and after China’s economic slowdown. This article offers actionable policy recommendations to inform future decisions based on these findings.

Research on the effect of multiple credit ratings from the perspective of financial regulatory systems in Chinese bond market.

This paper, starting from the effects of financial regulatory policies, considers the interaction between Chengxin_Moody and Lianhe_Fitch with the dual rating system and the multi-rating system, constructs a new ordered Logit model, and attempts to explore the impact of the Notice, the dual rating system and the multiple rating system on the probability of Chinese corporate bond defaults, rating upgrades, rating downgrades, and the magnitude of credit rating migrations. This study compares the effectiveness of different rating regulatory systems. Using nine thousand two hundred and sixty-two data of Chinese corporate bonds as the research samples. Empirical analysis and robustness test reveal the following findings: (1) The issuance of the Notice has a significant positive effect on the implementation of both the dual rating system and the multiple rating system, with a greater impact on the implementation of the multiple rating system; (2) The issuance of the Notice, along with the dual rating system and the multiple rating system, can all reduce the probability of corporate bond defaults, with the multiple rating system showing the best preventive effect against corporate bond defaults; (3) The dual rating system is more effective in promoting rating agencies to adjust rating behaviors, accurately correcting corporate bond ratings and effectively alleviating the issue of rating inflation. Competition among rating agencies intensifies rating shopping; (4) Under the dual rating system, rating agencies are more likely to expand the magnitude of credit rating upgrades and downgrades, enhancing the differentiation between high-quality corporate bonds and junk corporate bonds; (5) The selection of rating information from Chengxin_Moody and Lianhe_Fitch can appropriately adjust the degree of flexibility or tightening in response to rating regulatory systems. Chengxin_Moody demonstrates a more sensitive reaction compared to Lianhe_Fitch regarding the rating regulatory systems. This study provides valuable references for the formulation and evaluation of the effectiveness of Chinese corporate bond rating regulatory policies.

An Empirical Analysis of Invalid Voting in Municipal Elections

ABSTRACTThis paper examines blank and null voting in Brazilian mayoral elections using a large dataset covering 5570 municipalities between 2000 and 2020. The study finds that more competitive elections reduce blank and null percentages, with re‐election scenarios leading to more valid votes. Blank and particularly null voting are found to be more prevalent in municipalities where a second round is possible. Economic factors and age categories also contribute to determine unconventional voting patterns. The results also highlight important differences between blank and null voting patterns and between urban and rural areas, the later suggesting that more sophisticated voters tend to be less prone to invalidate the vote.

Econometrics of Climate Change Research

The econometrics of climate change explores the intricate interplay between economic activity and climate dynamics, offering empirical insights into the causes, impacts, and mitigation of global climate change. This research applies advanced econometric techniques to quantify the economic consequences of environmental changes and evaluate the effectiveness of climate policies. By leveraging panel data models, time-series analysis, and causal inference methods, this study investigates critical topics such as the economic costs of extreme weather events, the efficacy of carbon pricing mechanisms, and the role of renewable energy adoption in mitigating climate risks. Additionally, this research incorporates machine learning techniques to enhance predictive accuracy in modeling future climate-economic interactions. Through rigorous empirical analysis, the study provides actionable recommendations for policymakers to design sustainable strategies that balance economic growth with climate resilience. The findings contribute to a deeper understanding of the socioeconomic dimensions of climate change, emphasizing the importance of evidence-based decision-making in addressing one of humanity’s most pressing challenges.

A Comparative Analysis of the Sustainable Development of Urban Agriculture in Chengdu and Singapore

With the deepening global concern for sustainable development, urban agriculture has received more and more attention as an important combination of urban development and ecological environmental protection. This paper provides a comprehensive evaluation and comparison of two areas, Chengdu and Singapore, in terms of their capacity for sustainable development of urban agriculture through an empirical analysis method. To make a multidimensional evaluation index system, the study uses the hierarchical analysis method (AHP) and the fuzzy comprehensive evaluation method. The index covers important areas like the level of agricultural development, the intensity of applied inputs, the rate of energy use, the rate of resource utilization, and the level of agricultural factors. The results show that Chengdu performs significantly in terms of resource utilization rate and agricultural factor level, while Singapore performs better in terms of energy utilization rate. The study provides targeted recommendations, including strengthening scientific and technological innovation, policy support, and ecological and environmental protection, with the aim of providing references and lessons for the sustainable development of urban agriculture in the two places and other areas around the world.

Unleashing Innovation Under Sanctions: The Catalyst of National Culture

ABSTRACTThis article explores how national culture moderates the impact of economic sanctions on a targeted country's innovation performance, using a dynamic difference‐in‐difference approach. Our empirical analysis utilises a panel dataset spanning 213 nations from 2013 to 2019. We discover that economic sanctions generally have a detrimental effect on the innovation performance of targeted countries. However, not all types of sanctions have the same negative impact on innovation. Specifically, financial, arms, and travel sanctions exhibit this negative influence. Furthermore, we reveal that under the sanction circumstances, countries characterised by high power distance and high uncertainty avoidance cultures tend to experience a higher level of innovation performance. Our findings remain robust after conducting various tests. In our subsequent analyses, we focus on different types of sanctions and find that the favourable impact of power distance and uncertainty avoidance culture on the relationship between sanctions and innovation applies primarily to financial sanctions.

‘I Am Woman, Hear Me Roar!’: #March4Justice and the Desire for Law to Listen

In speaking to a particular moment in time, when women are increasingly frustrated that their stories are not heard by the law, this article analyses comments posted to the hashtag March4Justice, to offer a contextualist account of how women are locating themselves in (and against) the law by means of social media. It suggests that the use of this hashtag contributes to, and is evidence of, an overarching rallying discourse that pleads for law reform. The article showcases an empirical analysis of social media narratives to demonstrate that emotionality, narrativity, and communal identity weave together to represent (and constitute) a critique of law's ability to listen to the needs of women.

Adjusting Self‐Protective and Non‐Response Behaviours in Sensitive Prevalence Estimation by a Two‐Stage Multilevel Randomised Response Technique

SummaryWhen using the customary direct questioning approach to collect sensitive information in a survey, some respondents may have no problem disclosing their true status or opinion, while others may be reluctant to reveal them. The problem of response bias is likely to arise as some responses tend to be more socially desirable than truthful. Hence, the indirect questioning approach can be used to guarantee privacy protection and reduce the influence of social desirability bias. This paper introduces a new technique which combines, both the direct and indirect questioning approaches and allows Bayesian estimates of the prevalence of multisensitive attributes to be obtained by taking into account the estimated proportion of honest respondents under direct questioning. Our proposal stems from a real survey in Taiwan and is illustrated with two motivating examples concerning voting behaviours and sexual identity. The empirical analysis reinforces that the proposed two‐stage multilevel method is satisfactory in mitigating the effects of respondents' self‐protective behaviour, and produces results that are more reliable than those based on the traditional direct questioning approach and better than a previous version of the multilevel randomised response method that ignores the presence of cheating behaviours in the direct questioning stage.

The social gradient of trust in government: An empirical analysis based on the 2019 wave of the Chinese Social Survey

Recent research on trust in the Chinese government has increasingly focused on whether it can be seen as the civic foundation of political stability. Differing from previous studies discussing the authenticity of the high mean level of trust in China, this paper responds to this discussion by examining how trust in the government varies across different social classes. By analyzing data from the Chinese Social Survey, our research revealed a gradient in trust towards both central and local governments across social classes, with notably lower trust observed in the lower classes. The social gradient was more pronounced in trust towards local governments compared to the central government. Furthermore, we found that the implementation of equity-oriented policies was effective in bridging the gap in trust between classes. These findings not only indicate that highly stratified trust may lead to potential risk to political stability, but also provide valuable insights into government behavior and its consequences, which are essential for guiding the transformation of governmental functions in the future.

Corporate Sustainability Reporting and Financial Performance: An Empirical Analysis of Indian Listed Companies

This empirical analysis examines the relationship between corporate sustainability reporting and financial performance among Indian listed companies. In recent years, sustainability has gained prominence in corporate strategy due to increasing regulatory requirements, investor interest, and stakeholder expectations. The study explores how sustainability reporting, as disclosed through corporate sustainability reports (CSR), affects the financial performance of companies. Using a sample of Indian companies listed on major stock exchanges, the study analyzes data on sustainability disclosures, financial performance indicators (such as return on assets, return on equity, and market valuation), and various control variables. The analysis employs statistical methods, including regression models, to identify the correlation and potential causal linkages between sustainability reporting and financial performance. The findings suggest a positive relationship between comprehensive sustainability reporting and financial performance, indicating that companies with better sustainability practices tend to experience higher financial returns and market valuation. The results also highlight the role of regulatory frameworks, industry-specific factors, and company size in shaping the strength of this relationship. This study contributes to the growing body of literature on corporate sustainability by providing insights specific to the Indian context, where sustainability reporting practices are evolving. It suggests that companies engaging in transparent and detailed sustainability reporting may achieve not only environmental and social benefits but also financial gains, making sustainability a strategic priority for long-term value creation.