Empiricism Research Articles (Page 1)

A new probabilistic method for the generation of asymmetrical distributions: Empirical analyses using asymmetrical data

Abstract Optimizing the microchannel design of the next generation of chips requires an understanding of the in-situ property evolution of the chip-based materials under fast cooling. This work overcomes the conventional reliance on reheating data of the melt-quenched glasses by demonstrating direct observations of glass transition on cooling curves utilizing the most-advanced fast differential scanning calorimetry. By leveraging an MEMS chip sensor that allows for rapid heat extraction from microgram-sized samples to a purged gas coolant, the device is able to reach ultra-fast cooling rates of up to 40,000 K s -1 . Four thermal regions are identified by examining the cooling behaviors of two metallic glasses. This is because the actual cooling-rate of the specimen can differ from the programmed rate, especially at high setting-rates when the actual rates decrease before the glass transition is completed. We define the operational window for reliable cooling-curve analysis, build models with empirical and theoretical analyses to determine the maximum feasible cooling-rate, and demonstrate how optimizing sample mass and environment temperature broaden this window. The method avoids deceptive structural relaxation effects verified by fictive-temperature analysis and permits the capturing of a full glass-transition during cooling.

The rapid technological advancement of autonomous vehicles (AVs)—as exemplified by recent research such as Enhancing High-Speed Cruising Performance of Autonomous Vehicles Through Integrated Deep Reinforcement Learning Framework (IEEE Transactions on Intelligent Transportation Systems, 2025)—raises pressing societal questions about their acceptance. Although numerous studies have examined attitudes toward autonomous vehicles in recent years, most research is limited to a single country, age group, or small sample. As a result, there is still a lack of empirical analyses comparing multiple generations and conducted on large samples that would provide a comprehensive picture of the age-determining factors of social acceptance of AVs. The present study aims to fill this gap with a questionnaire survey of 8663 people, which examines the trust, technology acceptance, and willingness to use autonomous vehicles among the Baby Boomer, X, Y, Z, and Alpha generations using Likert scale items. We used the Kruskal–Wallis test to explore the differences between generations. Our results show significant differences: while Generations Z and Alpha show remarkable openness and trust in AV technology, the Baby Boomer generation shows marked skepticism. The novelty of the research is that it provides large-scale, multi-generational comparative data, thus shedding light from a new perspective on the cultural, experiential, and trust patterns that determine the social acceptance of autonomous vehicles, beyond technological factors. The limitations of the study include self-reported data collection, geographical limitations (Hungary), and the lack of actual driving experience among the Alpha generation, which may affect the validity of the responses. However, these factors point the way for future international and qualitative research. The results can provide practical guidance for decision-makers, developers, and urban planners in developing inclusive, generation-sensitive transportation policies and communication strategies.

Type of the article: Research ArticleAbstractThe relationship between corruption and investment has attracted growing scholarly attention amid global concerns over governance quality, institutional efficiency, and capital mobility. This paper aims to systematize and critically assess how the relationship between corruption and investment has been explored in academic literature from 2015 to 2024, without limiting either concept to specific forms or levels. A bibliometric analysis was conducted based on 1,535 journal articles indexed in the Scopus database. The study identifies publication trends, dominant keywords, and seven thematic clusters, which reflect major research areas such as institutional quality, foreign direct investment, sustainable development, public policy, and social outcomes. A focused subset of 184 articles, containing both corruption- and investment-related terms in their titles, served as the basis for thematic classification. Three main research approaches are identified: (1) investment-type studies, which overwhelmingly focus on foreign direct investment (FDI), while domestic and informal investments are rarely addressed; (2) causal-explanatory models, which emphasize economic and institutional determinants but largely omit cultural and behavioral variables; and (3) case-based empirical analyses, which are often concentrated on single-country contexts. China is the most frequently studied country, whereas Central Asia, the Middle East, the CIS region, Western Europe, and the Commonwealth are all significantly underrepresented. The findings reveal thematic fragmentation, conceptual bias toward FDI, and persistent geographical imbalance. The study provides a foundation for future research and supports the development of more diversified, context-sensitive approaches to understanding the corruption-investment nexus.

Abstract An extensive literature demonstrates a positive relationship between social capital and health. However, empirical analyses within the Italian context remain limited. This study contributes to the existing literature by expanding the empirical evidence on this topic for Italy, employing an ecological approach. Using data from 21 territorial units (19 regions and the autonomous provinces of Trento and Bozen) over the period 2011–2019, the study examines the role of bonding, bridging and overall social capital in shaping both objective health indicators (such as mortality rates, life expectancy and potential years of life not lost) and self‐reported health (specifically life expectancy in good health). The analysis also controls for regional socio‐economic factors and health resources, managed at the regional level in Italy. The findings reveal significant correlations between all dimensions of social capital and health outcomes, pointing out the importance of addressing regional disparities in social capital within health policy.

Background:Extraordinary increases in equity valuations, expenditure, capital intelligence, and public attention have accompanied the rapid expansion of generative AI since 2022. Central banks, international financial institutions, and market analysts increasingly refer to a possible “AI bubble,” raising questions about whether the boom is sustainable or likely to end in a sharp correction. Objective:To systematically review recent evidence and expert assessments on whether current AI-related investments and asset valuations exhibit characteristics of a financial bubble that is likely to burst in the near term. Methods:A systematic search was conducted (January 2023–November 2025) across Google Scholar, SSRN, and websites of major financial institutions and research organizations (e.g., Bank of England, International Monetary Fund, Congressional Research Service, McKinsey Global Institute, Goldman Sachs Research, California Management Review), search terms combined concepts for AI with bubbles, market valuations, and macroeconomic impact. Eligible sources included empirical analyses, official reports, and analytical commentaries that (a) discussed AI in the context of asset valuations or macroeconomic outcomes and (b) provided explicit arguments regarding bubble-like or fundamental characteristics. Data were extracted on (i) valuation or investment metrics, (ii) productivity and growth estimates, and (iii) explicit bubble assessments. A narrative synthesis was performed because of heterogeneity in methods and outcomes. Results:Thirty-five sources met the inclusion criteria, grouped into four categories: (1) financial stability and bubble-risk assessments by central banks and international institutions; (2) macroeconomic modeling and productivity studies; (3) management and strategy reviews of AI and productivity; and (4) market and investment research on AI valuations. Central banks and the International Monetary Fund (IMF) highlight “stretched” valuations and the risk of a sharp market correction, explicitly comparing current AI exuberance to the late-1990s dot-com boom. Macroeconomic and consulting studies project sizable, long-run productivity and GDP gains from AI, although meta-analytic evidence finds no robust relationship between AI adoption and aggregate productivity to date. Primary market analyses acknowledge bubble-like features, rapid valuation growth, circular spending, and concentration, but argue that fundamentals and current earnings distinguish AI from prior purely speculative episodes. Conclusions:The evidence supports a hybrid view: segments of AI-exposed equity markets display speculative excess. They are vulnerable to correction, whereas AI, as a general-purpose technology, has credible long-run economic value. A complete collapse akin to a pure asset bubble appears unlikely; a pattern similar to the dot-com cycle, over-valuation, correction, and subsequent consolidation with lasting productivity effects is more consistent with the available data. For policymakers, firms, and investors, the key challenge is not to bet for or against “AI” in the abstract, but to distinguish bubble-like exposures from durable, productivity-enhancing applications.

Gender-based violence (GBV) in South Africa continues to be a critical issue affecting the society. It is deeply entrenched within patriarchal structures shaped by historical injustices of colonialism and apartheid. Despite several legislative and policy initiatives aimed at addressing the scourge of GBV, media representations often perpetuate traditional gender stereotypes and biased narratives. Evidence from previous studies highlights the role of media in reinforcing gender inequalities by frequently sensationalizing violence and victim-blaming, thereby marginalizing the experiences of women. This paper contributes to ongoing debates on media framing by critically examining contemporary South African media narratives on GBV. The study will employ a framing analysis approach to scrutinize the key frames utilized by South African media in reporting GBV, drawing from significant scholarly works and news articles from IOL on GBV. Three empirical questions guide this study: (i) What frames are prevalent in media coverage of GBV in post-pandemic South Africa? (ii) Are there identifiable biases within these media narratives? (iii) How do media representations either reinforce or challenge entrenched patriarchal discourses? Analysis of selected news articles reveals persistent media practices that reinforce gender-biased stereotypes and diminish women’s agency and lived realities. Despite notable examples of counter-discourses emerging through digital activism, traditional media channels often undermine progressive efforts, perpetuating the perception that GBV remains inadequately addressed. This paper underscores the urgent need for more nuanced and transformative media practices that challenge systemic gender inequalities and promote genuine societal change in South Africa’s evolving post-apartheid context. The paper explores the growing GBV matters in the post-apartheid South Africa as presented in the media and argues that GBV matters. The paper pays particular attention to the inclusion and framing of women’s perspectives in the coverage of GBV in South Africa.

ABSTRACT The rapid advancement of information technologies has transformed how tourists access travel information, shifting from traditional search engines to diverse social media platforms. Social media communication index effectively captures tourists’ travel intentions and demand, offering a new data source for forecasting. However, how to measure and integrate this power into existing forecasting models remains unclear. To this end, this study introduces three social media communication indices – Weibo, WeChat, and Douyin – based on China’s leading platforms. Using six widely adopted forecasting models, empirical analyses are conducted at both the city and attraction levels. The results demonstrate that incorporating these indices significantly enhances forecasting performance, with the multi-platform social media communication indices outperforming their single-platform counterparts, highlighting the synergistic effect.

Abstract This article theorizes the concept ‘ethnolinguistic infusion’ as a language socialization and language management practice. Infusion involves community members incorporating fragments of their group language, in which most members have little or no competence, in the context of a different dominant language, with the potential effect of fostering ideological links among the individual, group, and language. I explain the metaphor, enumerate several characteristics, and offer a categorization of different types of infusion. I contextualize ethnolinguistic infusion among related constructs in language contact, sociolinguistics, and linguistic anthropology, including translanguaging, postvernacularity, and metalinguistic communities, I explain its relationship to ethnolinguistic repertoire, and I distinguish it from out-group-initiated phenomena like crossing and mock language. I demonstrate how ethnolinguistic infusion plays out in my research on American Jewish summer camps. I offer empirical questions for future research, and I conclude by arguing for the utility of ethnolinguistic infusion, both for academic analysis and for language activism. (Language and ethnicity, heritage language, symbolic language, emblematic language, language and group identity, Hebrew, infusion, loanwords, language contact, translanguaging, metalinguistic community, postvernacularity, endangered languages, language reclamation, language revitalization)

The manufacturing industry is undergoing a paradigm shift as crowdsourcing emerges as a viable alternative to traditional in-house product design methods. This paper presents a comprehensive framework for integrating crowdsourcing into manufacturing processes, with a focus on improving product design quality in small and medium enterprises (SMEs). A systematic literature review (following PRISMA guidelines) and empirical analyses were conducted to identify key factors influencing product design quality in both conventional and crowdsourced manufacturing settings. We formulated six hypotheses to examine relationships between traditional manufacturing, crowd-based design, and product design quality outcomes. Quantitative validation using statistical models and hypothesis testing indicates that crowdsourcing significantly enhances product design quality across five critical attributes – functionality, reliability, usability, maintainability, and creativity – compared to traditional approaches. We develop a formal mathematical model to represent product design quality as a function of these attributes, and we propose optimization strategies to maximize quality while managing cost and time constraints. The theoretical framework addresses quality control challenges on crowdsourcing platforms and provides practical solutions for manufacturing firms. Synthetic data-driven simulations and comparative analyses show that SMEs leveraging crowdsourcing can access a diverse talent pool, reduce operating costs, and accelerate innovation, all while maintaining high quality standards through rigorous quality management. The proposed integrated framework predicts overall design quality improvements of about 23%, cost reductions of 15–30%, and time-to-market acceleration of 25–40% relative to traditional design methods. This study offers novel insights and mathematical formulations that bridge quality management and crowdsourced manufacturing, yielding both theoretical contributions to applied manufacturing mathematics and actionable guidance for industry practitioners.

This study introduces a new probability model for the risk priority number (RPN) in Failure Mode and Effects Analysis (FMEA), addressing limitations of the traditional RPN calculation, which assumes independence among severity, occurrence, and detection scores. Leveraging sufficient statistics within a Bayesian framework, the proposed model captures the inherent dependencies among these components, providing a more realistic and flexible representation of risk. Simulation studies validate the estimator's superior accuracy and stability, while empirical analyses on both AI risk assessment and gas refinery fire risk data sets demonstrate its effectiveness and adaptability across diverse domains and sampling strategies. Model comparisons using p-values and the Akaike information criterion (AIC) confirm the new model as the best fit for categorical risk data, aligning naturally with our theoretical approach. The results suggest that this new model enhances the reliability and interpretability of FMEA risk assessments, providing a powerful tool for decision making and risk mitigation in complex safety-criticalsystems.

ABSTRACT This paper presents a novel exponential cum logarithmic estimator designed to enhance the estimation of the population mean in simple random sampling, leveraging auxiliary attributes. The Mean Squared Error of the suggested estimator is derived up to the first-order approximation. The study identifies the optimal conditions under which the Mean Squared Error of the proposed estimator is minimized. The performance of the proposed estimator is compared with several established estimators in the literature as Naik and Gupta (1996) estimators, Singh et al. (2007) estimators, Abd-Elfattah (2010) estimators, Zaman and Kadilar (2019) estimator, Zaman (2020) estimator, Audu et al. (2022) estimators and Singh, Gupta and Tailor (2023) Estimators. Preference regions are established where the proposed estimator outperforms these alternative estimators. The proposed estimator’s superior performance is validated through empirical studies using real-world data including the Health and Finance sectors, as well as simulation experiments through the Monte Carlo method using various probability distributions. Graphs are used to visualize the results from both the empirical and simulation analyses.

In this paper, we seek to explore whether merchant guild culture could be a driving force of corporate outward foreign direct investment (OFDI). Based on the institutional support perspective, we argue that firms could effectively leverage the institutional advantages stemming from merchant guild culture to actively participate in OFDI. Moreover, the promotion effects of merchant guild culture on corporate OFDI can work through three mechanisms: facilitating access to financial resources, promoting cooperation, and enhancing productivity. Empirical analyses based on data from Chinese publicly listed firms spanning 2007 to 2021 strongly support these arguments. In addition, we confirm that the promotion effect is more pronounced for firms in regions with lower levels of financial development, in highly competitive industries, and where clan power significantly influences merchant guild culture. Our study contributes to the literature on informal institutions, OFDI, and merchant guild culture.

Abstract In this crowdsourced initiative, 57 independent analysts used the same longitudinal dataset to address four major empirical questions in international business. For all four research questions, different analysts obtained substantial estimates in opposite directions, meaning that they could have drawn any conclusion at all had they conducted the project alone. Aggregating across the results obtained by different analysts pointed to an overall answer for two of the four research questions, although for one of the two questions, the evidence was more suggestive than conclusive. That said, the variability in results was not simply random, and could in some cases be meaningfully explained. Choices regarding how to operationalize variables played an important role in determining the empirical results, and expert analysts were more likely to report large positive effects. Rather than exhibiting a bias to confirm their pre-existing beliefs, analysts appeared to rationally update their beliefs considering the evidence. Overall, these findings empirically demonstrate the role of subjective researcher choices in shaping results in international business research yet also show that it is still possible to draw meaningful conclusions in science. We advocate for an open science of international business in which the consequences of subjective analytic choices are rendered as transparent as possible.

Abstract - Mechanical, Electrical, and Plumbing (MEP) systems collectively define the functional core of any modern building. Within this triad, plumbing systems play a crucial role in ensuring sustainable water management, sanitation, and occupant comfort. Yet, despite technological advancements such as Building Information Modelling (BIM), Virtual Design and Construction (VDC), and Lean Construction, plumbing design and coordination continue to face recurring challenges including rework, spatial conflicts, and inadequate compliance with standards. This review paper synthesizes existing research and case studies most notably, and multiple empirical analyses on plumbing systems in India, China, and Saudi Arabia to provide a comprehensive understanding of digital integration and performance optimization in plumbing systems. The review bridges technological and practical dimensions, covering BIM/VDC-based coordination, prefabrication, Lean process alignment, and sustainability- oriented plumbing design. By integrating both digital frameworks and field-level challenges, it develops a unified roadmap for efficient and resilient plumbing design in the next generation of MEP systems. Keywords: BIM, VDC, Lean Construction, Plumbing Systems, MEP Coordination, Prefabrication, Sustainable Water Management, Digital Twins, Interoperability, Building Performance.

Tourism enterprises are increasingly pressured to align competitiveness with sustainability, yet limited evidence exists from Central and Eastern Europe. This study investigates the role of eco-friendly orientation as a determinant of customer satisfaction and perceived competitiveness in the Visegrad Four (Slovakia, Czech Republic, Poland, and Hungary). Empirical research was conducted on a sample of 478 enterprises, including hotels, guesthouses, spas, agritourist facilities, and travel agencies. Data were collected between January and June 2025 using a standardized questionnaire and analyzed through descriptive statistics, factor analysis, Pearson correlation, multiple regression, and ANOVA. The findings demonstrate that enterprises adopting more extensive environmental practices report significantly higher customer satisfaction (r = 0.43, p < 0.01) and perceived competitiveness (r = 0.38, p < 0.01). Factor analysis identified three key dimensions of environmental orientation: ecological operations, ecological innovations, and ecological marketing. Regression analysis highlighted ecological marketing, particularly the adoption of certifications and eco-labels, as the strongest predictor of competitiveness. ANOVA revealed significant cross-country differences, with Slovak and Czech enterprises outperforming Polish and Hungarian counterparts. The results suggest that ecological initiatives are essential for long-term competitiveness, providing both strategic guidance for managers and policy implications for fostering supportive regulatory and financial frameworks across the region. This study makes a novel contribution by offering one of the first large-scale empirical analyses of the link between sustainability and competitiveness in Central and Eastern Europe, a region where such research is still scarce. Theoretically, it extends the application of Ecological Modernization Theory to the tourism sector, while practically it provides actionable recommendations for managers and policymakers on integrating eco-certification and ecological marketing into their strategies. These insights underline the dual role of environmental orientation as a driver of both customer satisfaction and competitive advantage.

Service area boundaries are the geographic delineation of a community water system’s (CWS) customer base. Lack of consistent and precise service area boundaries may affect how measures of water quality are geospatially assigned in academic or regulatory work, potentially hindering our ability to locate and accurately characterize disparities in drinking water provision. Though it is generally understood that more accurate boundaries would improve the analytical precision of drinking water quality analyses, it is unclear how the choice of boundary representations would impact conclusions of empirical analyses or the potential magnitude of bias. This paper aims to fill this gap by summarizing a set of novel drinking water quality metrics for arsenic, bacterial detection, disinfection byproduct formation, lead, nitrates, PFAS, and health-based violations of the Safe Drinking Water Act. We compare these drinking water measures across service area assignment methods including the use of county served, zip codes served, the EPIC/SimpleLab dataset, boundaries created by the U.S. Geologic Survey, and a national data layer produced by EPA’s Office of Research and Development. Conclusions regarding the presence of a disparity depend on the service area boundary selected for at least one demographic group for six of seven drinking water quality measures in this analysis. This paper helps to motivate the importance of producing, maintaining, and updating a high-quality, nationally consistent geodatabase of drinking water system service areas.

Technical Debt (TD) represents the effort required to address quality issues that affect a software system and progressively hinder code evolution over time. A pull request (PR) is a discrete unit of work that must meet specific quality standards to be integrated into the main codebase. PRs offer a valuable opportunity to assess how developers handle TD and how codebase quality evolves. In this work, we conducted two empirical analyses to understand how developers address TD within PRs and whether TD is effectively managed during PR reviews by both developers and reviewers. We examined 12 Java projects from Apache. The first study employed the SonarQube tool on 2,035 merged PRs to evaluate TD variation, identify the most frequently neglected and resolved types of TD issues, and analyze how TD evolves over time. The second study involved a qualitative analysis of review threads of 250 PRs, focusing on the types of PRs that frequently discuss TD, the characteristics of TD fix suggestions, and the reasons some suggestions are rejected. Our findings reveal that TD issues are prevalent in PRs, following a ratio of 1:2:1 (reduced: unchanged: increased). Among all TD issues, those related to code duplication and cognitive complexity are most frequently overlooked, while code duplication and obsolete code are the most commonly resolved. Regarding PR code review, we found that around 76% of review threads address TD, with code, design, and documentation being the most frequently discussed areas. Additionally, 96% of discussions include a fix suggestion, and over 80% of the discussed issues are resolved. These insights can help practitioners become more aware of TD management and may inspire the development of new tools to facilitate TD handling during PRs.

In this empirical study, I develop theorizing on the conditions under which employees’ informal, competence-based power in strategically core roles enhances organizational performance. I propose that in contexts characterized by high task specialization and interdependence, employees with high informal power positively influence performance through higher motivation and improved coordination. However, this effect is weaker when managers also hold high informal power or emphasize hierarchical structures, as these factors create power conflicts that undermine employees’ informal influence. Conversely, the positive effect is stronger when employees receive negative performance feedback, which they perceive as a challenge to their competence, leading to stronger association between employee informal power and performance. Empirical analyses provide strong support for these propositions, showing that employees’ informal power in strategically core roles can be a key driver of performance improvements, but only under specific organizational conditions.

The paradox of artificial intelligence and environmental performance: The critical role of energy transition and institutional quality.