Empirical Analysis Research Articles

Contributing factors to PKK’s failure to implement Mao’s protracted people’s war strategy

ABSTRACT This article investigates how and why Mao’s protracted people’s war strategy of three stages (strategic defence, strategic balance, and strategic offence) was chosen and implemented as the main strategy by PKK and why PKK failed in the first stage of this strategy. It explores this failure using an empirical content analysis of primary sources, specifically the documents published by the PKK. The comparative analysis of Mao’s strategy from a military perspective, along with the PKK’s implementation of it, is a main focus of this research. The principal question is ‘What factors contributed to PKK’s failure in implementing Mao’s protracted people’s war strategy, specifically in failing to move beyond the first stage?’ The analysis pinpoints six factors contributing to PKK’s military failure, with the most notable being the shift in Turkish Armed Forces’ strategic approach to counterterrorism, as implemented in 1993.

Relationship between Compliance with Benford's Law and Timeliness of Financial Statements: An Empirical Analysis

Relationship between Compliance with Benford's Law and Timeliness of Financial Statements: An Empirical Analysis

Empirical analysis of MSMEs tax non-compliance from Islamic outlook

Purpose With the presence of the religiously mandated practice of zakat, there are ongoing debates on whether governments have the right to levy taxes, especially in predominantly Muslim communities. This study aims to explore the moderating effect of Islamic religious outlook (IRO) on relationships between the determinants, such as governance quality (GQ), service quality (SQ), tax penalties (TP),and tax rate (TR), and tax non-compliance (TNC) behaviour. Design/methodology/approach Adopting a cross-sectional design through a quantitative method, the authors collected 341 surveys from micro, small, and medium enterprises (MSMEs) and analysed them using SmartPLS software. Findings Interesting outcomes were obtained from this study. The authors observed that IRO exerts a positive impact on TNC behaviour among MSMEs. Likewise, the economic factor (TP) was positively associated with non-compliance behaviour, whereas TR, SQ and GQ failed to report significant conclusions. With integrating IRO as a moderator into the model, it was revealed that the IRO-moderated interactions of GQ, SQ, TP and TR on TNC behaviour are significant. Practical implications The results offer substantial actionable insights for tax authorities and governments in developing nations, particularly those with a predominantly Muslim population. This study specifically aims to enhance the understanding of the critical determinants that significantly impact taxpayers’ behaviour, aiming to strengthen tax collection. Some policies must be implemented to mitigate non-compliance with tax rules, especially among MSMEs Muslim-majority countries. For instance, tax rebates to those who paid zakat might be an effective way to enhance compliance, whereas regulations need to be reconsidered to avoid potential conflict between zakat and taxes. Originality/value To the best of the authors’ knowledge, this attempt is the first to empirically analyse IRO on whether governments have the right to levy taxes and its moderating role in tax literature. The insights from this study can advance future academic endeavours and also assist in formulating actionable strategies to boost tax funds.

Is there a beauty is beastly effect in electoral success? An empirical analysis of the German federal elections 2005 to 2021.

Research shows that attractive women may face disadvantages in male-dominated contexts or those stereotypically associated with masculinity, because they tend to be ascribed more stereotypically feminine character traits and capabilities. This is known as the "beauty is beastly effect." However, its impact on political elections remains largely unexamined. This study investigates whether such an effect exists for female candidates in Germany, where political competition is male-dominated and rewards stereotypically masculine traits. Using a comprehensive data set from the 2005 to 2021 federal elections, we empirically test for interactions between gender and physical attractiveness. Despite extensive multilevel analyses, no evidence was found for the "beauty is beastly effect" in this context. Nevertheless, positive main effects suggest female candidates may still face disadvantages. Possible explanations for these findings are discussed.

EMERGING TRENDS OF RISK MANAGEMENT IN THE INVESTMENT SECTOR IN INDIA: A REVIEW PAPER

This paper explores the evolving landscape of risk management in India's investment sector, focusing on emerging strategies, technologies, and frameworks used to mitigate financial uncertainties. With rapid digitization, increased regulatory scrutiny, and rising ESG (Environmental, Social, Governance) awareness, the traditional approaches to risk management are undergoing a significant transformation. Drawing from secondary sources, surveys, and institutional practices, the review identifies the primary tools, techniques, and challenges shaping modern investment risk practices. The paper also outlines research gaps and suggests future directions for more robust and adaptive risk frameworks. Key findings highlight the importance of integrating AI, real-time analytics, and sustainable governance models for investor confidence and market stability. By identifying strengths and shortcomings in current practices, the paper aims to provide a comprehensive perspective on the sector's readiness to manage risk proactively rather than reactively. Also, it highlights research gaps related to the uneven adoption of risk technologies and the limited empirical analysis of behavioral risk dynamics. The findings suggest that India's financial ecosystem is transitioning toward a more adaptive, resilient, and transparent model of risk governance, yet requires greater institutional collaboration and innovation to withstand future disruptions. Keywords: Risk Management, Investment Sector, India, Artificial Intelligence, ESG Integration, Financial Technology, Regulatory Compliance, Cybersecurity, Behavioral Finance, Emerging Risks.

Organizational legacies, ideology, and policy stances on universal welfare of Western European political parties, 1970–2019

ABSTRACT This article introduces and empirically tests a conceptual framework that explains political parties’ stances on universal welfare as correlates of their organizational legacies that are manifest in the residual presence of clientelism and mass membership in their current voter mobilization strategies. Party positions on the broader left-right spectrum are incorporated as a mediating factor that shapes the relationship between party organization and welfare policies. The empirical analysis draws on data from the V-Party project, covering 269 elections across 20 Western European countries between 1970 and 2019. Employing a series of country-party-level random-effects models, we find that mass organization is positively associated with pro-welfare stances, whereas the negative relationship between clientelism and pro-welfare stances is conditional on ideology. This relationship – particularly pronounced in single-shot clientelism – holds for centre-right parties and fades away for the left-wing parties. Active local community presence and ties to affiliate organizations, mitigate the negative relationship between clientelism and pro-welfare positions. However, when parties operate under a clientelist mode of functioning, the presence of local party offices reduces support for universal welfare policies. Overall, our empirical findings highlight the complex interplay between organizational legacies and ideological orientations in shaping welfare policy positions among Western European parties.

Medical Bankruptcy in India: Causes, Empirical Analysis, and Statistical InsightsMedical Bankruptcy in India: Causes, Empirical Analysis, and Statistical Insights

Medical bankruptcy is a situation where individuals face financial distress due to overwhelming medical expenses that they are unable to pay. It occurs when the cost of medical treatments, surgeries, medications, and hospital bills becomes so high that it leads to severe financial strain. Lack of adequate health insurance coverage or unexpected medical emergencies can contribute to the occurrence of Chapter 7 and Chapter 13 medical bankruptcy. This paper is delved with the objectives to revisit the national scenario of medical bankruptcy along with identification of several factors affecting it and to understand the impact of the factors contributing to medical bankruptcy of individuals along with suggesting some remedial measures to get rid from the bankruptcy situation. This research on medical bankruptcy is grounded in the collection and analysis of primary survey over 105 Indian nationals with diverse nature of profession and income levels and gathered their views and opinions on the related topic to understand the association between the variables affecting the responses to the issue under research. Thus we have classified our sample into certain categories –age, affiliation, and level of income. Further we proceed to study the impact that these categories exert on the opinion on the research issue, through measures of association- viz Goodman Kruskal Gama measure on ordinal data and Pearson's Chi- square test on nominal data for the achievement of diverse objectives of this research study. This approach ensures the authenticity and specificity of the findings, tailored to the details factors affecting medical bankruptcy cases using R- software.

Research on the Impact of Customer Experience on Customer Stickiness in The Context of E-Commerce Live Streaming

With the rapid development of the digital economy, live-streaming e-commerce, as an emerging business model, is facing practical challenges of insufficient customer stickiness. Existing research has mainly focused on the influence of pre-purchase and during-purchase customer experiences on customer behavioral stickiness, while neglecting comprehensive studies on the impact of full-cycle customer experience—including pre-purchase, purchase, and post-purchase stages—on both psychological and behavioral customer stickiness. Based on the SOR (Stimulus-Organism-Response) theory and using empirical analysis, this study explores the influence mechanisms of customer experience throughout the full purchase cycle on psychological and behavioral stickiness. The findings reveal that customer experience at each stage of the purchase cycle has a significant positive impact on both psychological and behavioral stickiness. Perceived value plays a mediating role in this relationship. Social presence—the perception of others' existence—positively moderates the relationship between customer experience and perceived value, particularly in the pre-purchase and post-purchase stages. However, its moderating effect on the relationship between customer experience during the purchase and perceived value is not significant.

Family Firms′ Exploratory Innovation in Relaxation and Urgency Environments: Evidence from Chinese Manufacturing Firms

The innovation behavior of family firms has long been a focal point in both academic research and practical applications. Based on the socioemotional wealth theory, this study aims to empirically analyze the differences in exploratory innovation between family and non-family firms while exploring how internal and external environmental factors, namely overperformance duration and industrial competition, moderate this relationship. We conducted an empirical analysis using data from manufacturing firms listed on China′s A-share market from 2009 to 2018. The results indicate that family firms exhibited a lower propensity for exploratory innovation compared to that of non-family firms. Furthermore, the negative relationship between family firms and exploratory innovation was more pronounced in relaxed internal environments characterized by overperformance duration, while this negative relationship was alleviated in urgent external environments marked by intense industrial competition. This study contributes a fresh perspective to the literature on family firm innovation and provides valuable insights for policymakers and family firm managers seeking to enhance innovation competitiveness.

Evaluating Urban Heat Island Mitigation Policies in Heritage Settings: An Integrated Analysis of Matera

This study investigates the environmental parameters that contribute to the Urban Heat Island (UHI) effect in historic environments, with a particular focus on the UNESCO World Heritage City of Matera. The complex urban morphology of Matera, with its narrow streets and underground buildings, generates distinctive microclimates that intensify the UHI phenomenon, posing challenges for urban planning and heritage conservation. The main objective of the research is to identify which environmental parameters interact with Matera’s architectural and urban characteristics to intensify the UHI, and to propose mitigation strategies that balance heritage conservation with environmental sustainability. The research follows a mixed methodological approach in two phases. The first phase consisted of a comprehensive literature review, identifying gaps in previous studies and developing a methodological framework combining quantitative and qualitative techniques. The second phase involved empirical analysis using advanced techniques such as 3D laser scanning to model urban morphology, satellite image analysis to map the spatial distribution of the UHI, and the integration of historical and real-time meteorological data. The results show significant correlations between urban morphology and UHI intensity, suggesting strategic interventions such as green roofs and reflective materials to mitigate the effects. These findings provide valuable information for urban planners and policy makers, and highlight the importance of integrating sustainable approaches into heritage conservation.

Analysing the legal meaning of ‘copying’ through an empirical study of UK substantial similarity copyright cases and psychological explanations of litigant trends

Abstract The substantial similarity test has been criticized as difficult to apply. Nevertheless, it plays a central role in deciding the extent to which works similar to existing copyright works are improper copies or new works that should be allowed. This article provides an empirical analysis of UK substantial similarity copyright cases, contributing to this discussion by revealing how this test is applied in practice. What type of substantial similarity copyright cases tend to be brought by litigants, what factors are relevant in the judgments and what patterns of claimant and defendant losses are revealed? It finds that courts are slightly more likely to find no copyright infringement, largely due to defendants successfully arguing that similarities are commonplace and/or that they created the work independently. However, defendants are likely to lose at the substantial similarity stage where courts find there has been copying and that such copying is substantial. Adopting an interdisciplinary approach, the article argues that the results reflect psychological findings of human tendency to overvalue their own creativity and that courts adopt an approach to creativity that reflects a psychological approach to creative processes. The article demonstrates how this psychological framework of the processes involved in creativity can illuminate our understanding of the idea/expression divide and its common criticisms.

The Analysis of Exchange Rate Fluctuations in China's Economic Fluctuations

This study focuses on the influenced factors of exchange rate fluctuations in China's economic fluctuations. Currently, the impact factors of exchange rate fluctuations within the economic system is becoming increasingly significant. The aim of this paper is to explore the factors influencing exchange rate fluctuations in the process of China's economic fluctuations, and to solve the specific impact mechanism and degree of China's economic fluctuations on exchange rate fluctuations. By constructing a dynamic model and combining macroeconomic data for empirical analysis, quantitative methods are used to investigate the degree of influence of China's economic development on exchange rate fluctuations, as well as the changing trends under different economic cycles. This study uses econometric methods, combined with time series analysis and regression analysis techniques, to conduct in-depth research on the impact of exchange rate fluctuations in China's economic fluctuations. At the same time, relevant theoretical models are used to explain and verify the research results, ensuring the scientific and reliable nature of the research methods. The results of the study show that the impact of exchange rate fluctuations in China's economic fluctuations cannot be ignored, and macroeconomic factors such as economic growth, export trade, and international competitiveness have a significant impact on exchange rate fluctuations in China. The study provides important reference for further improving China's economic policies, and provides a new theoretical perspective and empirical support for related fields of research.

Empirical Analysis of Foreign Direct Investment and Its Influencing Factors: A Case Study of Henan Province

In recent years, the foreign direct investment attracted by Henan Province has been increasing and has become an important driving force of Henan Province's economic development, but the scale still has a large gap with developed provinces, the analysis of its influencing factors is beneficial to our further introduction of investment and future policy guidance. Based on theoretical analysis, this paper selects six variables as the influencing factors of FDI, including the scale of economic development, the state of economic development, labour cost, infrastructure level and trade policy orientation, and openness to foreign trade, etc. Using the time series data of Henan Province from 1995 to 2021, the relationship between FDI and the above six explanatory variables is empirically The results of the empirical study show that The empirical results show that the scale of economic development represented by GDP has a significant positive impact on FDI in Henan Province, while labour cost represented by regional per capita wage has a negative impact on FDI in Henan Province, while other factors do not have a significant impact on FDI.

Plant Stem Disease Detection using Deep Learning

INTRODUCTION In recent years, advancements in machine learning have revolutionized the field of plant biology and agriculture, particularly in the automated classification of plant stems and the identification of associated diseases. Among the various machine learning techniques, Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) have emerged as prominent tools for handling complex image-based classification tasks[1]. Support Vector Machines (SVMs) are widely recognized for their ability to construct optimal hyperplanes in high-dimensional feature spaces, making them effective in tasks where the separation of classes is crucial. SVMs have been successfully applied in diverse domains, including image classification, due to their robustness and ability to generalize well with appropriate kernel functions[9]. Convolutional Neural Networks (CNNs), on the other hand, have gained immense popularity in recent years, particularly in computer vision tasks. CNNs are designed to automatically learn hierarchical representations of data, especially in the context of images, by applying convolutions over input images and progressively extracting features at different spatial levels. This characteristic makes CNNs particularly well-suited for tasks such as object recognition and image classification without the need for extensive handcrafted feature engineering[6]. In the domain of stem classification and disease identification in plants, both SVMs and CNNs offer distinct advantages and trade-offs. SVMs provide a strong theoretical foundation with well-understood principles of margin maximization and kernel methods, which can be advantageous in scenarios with limited training data or when interpretability of the model is critical. On the other hand, CNNs excel in capturing intricate patterns and spatial relationships within images, which is particularly beneficial in tasks where the visual appearance of stems and subtle disease symptoms play a significant role[2]. Through empirical analysis on a diverse dataset encompassing various plant species and stem conditions, we assess the strengths and limitations of each approach, providing insights into their applicability and effectiveness in real-world agricultural and environmental monitoring scenarios. The findings presented here contribute to a deeper understanding of the capabilities of SVMs and CNNs in automated plant biology and crop management applications[1].

Unbundling ESG for Fiduciary Integrity: An Empirical Analysis on the Representativeness of ESG Pension Investment Legislation

The growing divide in U.S. politics has significantly influenced how states administer their pension funds. Republican-controlled states have introduced laws prohibiting state pension funds from considering non-pecuniary factors. These states primarily cite concerns about potential breaches of fiduciary responsibility as their rationale. Conversely, Democratic-led states have taken the opposite approach, actively promoting or requiring the incorporation of Environmental, Social, and Governance (ESG) factors in pension investment decisions. This stark contrast reflects divergent perspectives on whether pension beneficiaries prioritize financial gains above all else or if they would accept potentially lower returns to support environmental and social objectives. This study empirically evaluates these assumptions with two large U.S. pension plan participant samples, using conjoint analysis—a cutting-edge method for preference measurement—to empirically assess assumptions about pension plan participants’ attitudes towards ESG features and their willingness to sacrifice profits to endorse environmental or social causes. This research is the first comprehensive U.S. effort to apply this method in the context of ESG investing with representative samples of the U.S. The findings show a clear partisan divide in attitudes regarding investment restrictions on firearms and fossil fuels. However, broad support exists for companies addressing child labor, living wages, and gender pay equality when these social goals are presented with specificity. In contrast, when social issues are framed more generally as “labor rights,” Republican participants tended to prioritize profitability. These results indicate that misconceptions about ESG and unawareness of social externalities may influence public support for anti-ESG legislation. They also reveal that neither ESG mandates nor bans may reflect the diverse preferences of pension beneficiaries. Properly addressing fiduciary concerns requires dissecting ESG into its components to match beneficiaries’ investments with their values more effectively.

Research on Integration and Optimization of Intelligent Warehouse and Logistics Information System in E-commerce Supply Chain

The traditional warehousing and logistics management model has been difficult to meet the needs of efficient operation, and the integration and optimization of intelligent warehousing and logistics information system has become the key. In this paper, an integrated optimization scheme of intelligent warehousing and logistics information system based on Internet of Things (IoT), big data, cloud computing and artificial intelligence (AI) is proposed. By designing a four-layer system integration architecture (data acquisition layer, data processing and analysis layer, application service layer and user interaction layer), the seamless connection and efficient cooperation between warehousing and logistics are realized. The optimization algorithm combines IoT, big data, cloud computing and AI technologies to optimize warehousing management and logistics operation with the goal of minimizing inventory cost, maximizing logistics efficiency and improving customer satisfaction. The empirical analysis takes E-commerce enterprise A as a case. Through one-year data collection and analysis, the results show that the optimized inventory is reduced, the times of excess inventory and shortage are reduced, the transportation path is more reasonable, the transportation time and cost are significantly reduced, the logistics distribution is more punctual and accurate, and the customer satisfaction is improved.

Faster? Softer? Or More Formal? A Study on the Methods of Enterprises’ Crisis Response on Social Media

Algorithmic recommendation mechanisms of social media platforms, viral diffusion of user-generated content (UGC), and real-time public opinion pressures are fundamentally deconstructing the traditional corporate crisis response paradigm that used to rely on one-way statements and delayed reactions. This compels enterprises to elevate their crisis response standards and construct new response frameworks. Based on an empirical analysis of 3,135,675 social media dissemination data points from 94 corporate crisis incidents, this study explores effective crisis response patterns for enterprises through three dimensions: response timing, methods, and content. The key findings indicate that traditional crisis response timelines prove inadequate for social media scenarios, whereas intervention during the ascending phase of dissemination significantly curtails crisis propagation cycles. Beyond formal statements, informal responses demonstrate equivalent mitigation effects, with combined formal–informal approaches yielding optimal outcomes. The comparative analysis of four content strategies (downplaying, supporting, denying, and reframing) reveals differentiated impacts on dissemination volume and duration, highlighting an inherent trade-off between these parameters. This research contributes to the crisis management theory in social media contexts while providing actionable guidance for enterprises to establish systematic crisis response methodologies. The results emphasize temporal sensitivity in response deployment, strategic content formulation, and multimodal communication integration.

Stakeholder Engagement and Organizational Performance in Semi-Autonomous Government Agencies: An Empirical Analysis in Uganda

Semi-Autonomous Government Agencies (SAGAs) play a critical role in delivering public services with operational independence, yet their effectiveness varies significantly across regions. In Uganda, agencies such as the Uganda Revenue Authority (URA), Uganda National Roads Authority (UNRA), and National Medical Stores (NMS) have demonstrated both commendable achievements and persistent performance challenges. Issues such as corruption, financial mismanagement, and political interference have highlighted the need for stronger governance and stakeholder engagement mechanisms. This study investigates the relationship between stakeholder engagement and organizational performance in Uganda’s SAGAs, with a specific focus on the healthcare sector. It seeks to assess how stakeholder participation influences transparency, efficiency, and service delivery outcomes. Using a mixed-methods approach, the study combined qualitative data from interviews and document reviews with quantitative performance indicators to evaluate stakeholder engagement practices across selected SAGAs, particularly the National Medical Stores. The analysis also incorporated recent audit findings from the Office of the Auditor General for the financial year ending June 2024. Findings indicate that strong stakeholder engagement contributes positively to performance metrics such as supply chain efficiency and accountability. For example, despite infrastructure and funding challenges, the NMS improved essential drug availability in rural areas by over 70%. However, the audit revealed severe issues including UGX 8.9 billion in expired medical supplies and systemic pension overpayments totaling UGX 18.6 billion, reflecting significant lapses in oversight. While stakeholder engagement enhances organizational responsiveness and accountability, its impact is undermined by governance gaps. Addressing these systemic issues is crucial for improving the performance of SAGAs and ensuring sustainable public service delivery in Uganda Keywords: Stakeholder Engagement, Organizational Performance, Semi-Autonomous Government Agencies (SAGAs), and Public Sector Accountability

AI for Climate Resilience: Predictive Analytics for Global Risk Reduction and Sustainable Development

Abstract: This research paper explores the pivotal role of artificial intelligence (AI) in enhancing climate resilience through advanced predictive analytics and data-driven decision-making. As climate change intensifies the frequency and severity of extreme weather events, AI technologies—such as machine learning, deep learning, and neural networks—are being leveraged to model complex environmental systems, forecast climate-related risks, and inform mitigation and adaptation strategies. The paper examines the integration of satellite imagery, remote sensing data, and real-time monitoring with AI algorithms to predict disasters like floods, droughts, and wildfires with greater accuracy. It also investigates AI's contribution to sustainable development by optimizing resource allocation, improving early warning systems, and supporting climate-smart infrastructure. Through case studies and empirical analysis, this study highlights how AI-driven climate intelligence can reduce vulnerabilities, guide global policy, and accelerate the transition to a resilient and sustainable future. Keywords: Artificial intelligence, Climate resilience, Predictive analytics, Sustainable development, Climate risk modeling, Disaster forecasting, Machine learning, Deep learning, Environmental monitoring, Early warning systems, Climate adaptation, Risk reduction, Smart infrastructure, Climate change mitigation, Remote sensing

A General Conformable Black–Scholes Equation for Option Pricing

Since the emergence of the Black–Scholes model (BSM) in the early 1970s, models for the pricing of financial options have been developed and evolved with mathematical tools that provide greater efficiency and accuracy in the valuation of these assets. In this research, we have used the generalized conformable derivatives associated with seven obtained conformable models with a closed-form solution that is similar to the traditional Black and Scholes. In addition, an empirical analysis was carried out to test the models with Mexican options contracts listed in 2023. Six foreign options were also tested, in particular three London options and three US options. With this sample, in addition to applying the seven generalized conformable models, we compared the results with the Heston model. We obtained much better results with the conformable models. Similarly, we decided to apply the seven conformable models to the data of the Morales et al. article, and we again determined that the conformable models greatly outperform the approximation of the Black, Scholes (BS), and Merton model with time-varying parameters and the basic Khalil conformable equation. In addition to the base sample, it was decided to test the strength of the seven generalized conformable models on 10 stock options that were out-sampled. In addition to the MSE results, for the sample of six options whose shares were traded in the London and New York stock markets, we tested the positivity and stability of the results. We plotted the values of the option contracts obtained by applying each of the seven generalized conformable models, the values of the contracts obtained by applying the traditional Heston model, and the market value of the contracts.