Empirical Study Research Articles

Empirical Study on Option Pricing under Markov Regime Switching Economics

Empirical Study on Option Pricing under Markov Regime Switching Economics

From RNN techniques to pre-trained models: emphasis on the use in Arabic machine translation

<p>Neural Machine Translation (NMT) has gained increasing attention in recent years due to its promising performance over conventional approaches such as Statistical Machine Translation. Nevertheless, when applied to languages with different structures, such as the pair (English, Arabic) that interests us in this work, its efficiency is degraded.<br />Alternatively, the use of unsupervised pre-training of large neural models has recently made a significant leap forward in the field of Natural Language Processing (NLP). By warm starting from published checkpoints, NLP practitioners have been able to push the state-of-the-art boundaries on multiple benchmarks while simultaneously saving considerable computational time. The emphasis so far has been mainly on natural language understanding problems. In this paper, we show the efficacy of pre-trained checkpoints for Arabic Machine Translation. We have evolved a transformer-based sequence-to-sequence model which is compatible with pre-trained checkpoints publicly available for Arabic Bidirectional Encoder Representations from Transformers (AraBERT) and Arabic Generative Pre-trained Transformer (AraGPT) and conducted a thorough empirical study on the usefulness of initializing our Arabic MT model, both encoder and decoder, with these checkpoints. Our models yield new state-of-the-art results in Arabic MT.</p>

Museum visit intervention in K-12 education: a scoping review

This scoping review aims to provide an overview of empirical studies on worldwide museum visit intervention in K-12 education. The study employed Mendeley citation software to identify the articles in the database. A meta-analysis PRISMA statement is used for reporting the items. Out of 135 possibly rich articles, the present study reviewed 18 studies that met the inclusion criteria and were subjected to descriptive and content analyses published between 2017 and 2021. Most of the studies are experimental and from primary school contexts. It is revealed that science is the subject matter context majority of the studies, but philosophy, disaster management, language, and environmental science are also represented. The content analysis resulted in the following learning and social outcomes. It states that social outcome is explored chiefly, followed by learning outcome. The findings indicate that museum visit intervention positively impacts students learning and social outcome. The review also identifies the need for further research on museum visit intervention in the Asia Pacific region.

Optical sensor to improve the accuracy of non-invasive blood sugar monitoring

Optical sensors offer a painless method of monitoring blood glucose levels using various light technologies to analyze blood characteristics without penetrating the skin. The literature review part reflects the progress in optical sensor technology evaluates its potential in blood glucose monitoring by overcoming the limitations of conventional methods and recognizes the challenges and future prospects in this rapidly developing area of research. The results of empirical studies are then presented. The methodology is presented as a non-invasive method of blood glucose monitoring based on near-infrared spectroscopy. To precisely evaluate blood glucose concentrations, spectroscopy techniques involving absorption and reflection are employed at wavelengths 450, 900, 1350, and 1800 nm. After absorption and reflection of glucose molecules, light is generated. An experimental study of different samples revealed a linear relationship between the final output voltage and sugar concentration. The results demonstrate a correlation between blood glucose level and signal intensity after transmission.

Impact of industrial robot on labour productivity: Empirical study based on industry panel data

This empirical analysis explores the impact of industrial robots on labour productivity using panel data of 17 Chinese industries from 2006 to 2021. The results reveal that the development of industrial robots significantly improves labour productivity; a series of robustness tests validate this outcome. However, the impact of industrial robots on labour productivity varies across industry types. The influence coefficient of the low-density robotics industry is larger than that of the high-density robotics industry. Furthermore, although the scale of industrial robot usage before 2012 was smaller than that after 2012, its effect on labour productivity was more significant. Our findings indicate the possibility of diminishing marginal effect of industrial robots in promoting labour productivity. The mechanism analysis demonstrates that human capital level has a complete intermediating effect between industrial robots and labour productivity. Thus, industrial robot applications can contribute to labour productivity by optimising human capital structure. These findings provide crucial insights for governments and policy makers to improve labour productivity and economic growth.

Mass evacuation planning for disasters management: A household evacuation route choice behavior analysis

This paper presents a multi-methodological approach to analyzing household evacuation route choice behaviors and the related decision-making for mass evacuation planning in disaster management. Specifically, the proposed multi-methodological approach integrates a syncretic risk perception conceptual model with a household-based group utility evolution model. The dynamics of household evacuation route choice are explored in two stages, namely the individual utility initiation and accommodation. The unique feature of the proposed approach is that it links individual risk perception, survival psychology, and group behavioral dynamics together to characterize household decision-making about choosing the evacuation routes. An empirical study coupled with experiments is conducted to help validate the proposed household evacuation route choice behavior model. Through various designed disaster scenarios, the applicability and strengths of the proposed model for mass evacuation planning and management are demonstrated. Particularly, the mean relative prediction errors of the proposed model with respect to household-based evacuation route switching probability and path flow splits are 3.79% and 6.88%, respectively, all within an acceptable range of ±10 %. This demonstrates that the proposed model and analytical results align closely with household choice behavior.

An Empirical Study of Combinational Load Forecasting in a City Power Company of China

An Empirical Study of Combinational Load Forecasting in a City Power Company of China

Differentiated instruction in reading in elementary schools: a systematic review

<span lang="EN-US">Increasingly diverse learners in today’s classroom might require teachers to implement differentiated instruction (DI) to create an effective learning process. This systematic review provides an overview of how DI was implemented in reading instruction in elementary education. For this purpose, the researchers systematically searched five databases (APA PsycINFO, Education Research Complete, ERIC, Scopus, and Web of Science). Based on the inclusion criteria, 28 empirical studies from 2002 to 2022 were selected for review. To be included in the review, the studies should be empirical, written in English, peer-reviewed, focusing on reading in elementary education, and include students with or without disabilities. The researchers created content coding in Microsoft Excel spreadsheet to extract relevant information from the selected studies. The findings indicated that implementing DI in reading instruction to elementary school students includes differentiation in content, process, and product, increased quality and quantity of explicit reading instruction, assessments informed instructions, and integrating DI with other instructional models. This emphasizes that although differentiation might seem complex, it is possible to implement it in reading instruction.</span>

Application of Generative Artificial Intelligence (GenAI) in Language Teaching and Learning: A Scoping Literature Review

This scoping literature review examines the application of Generative Artificial Intelligence (GenAI), a disruptive technology, in language teaching and learning. Since its launch in November 2022, GenAI has captured global attention with OpenAI's ChatGPT, powered by the generative pre-trained transformer-3 (GPT-3) large-language model. The emergence of GenAI holds immense implications across various domains, including language education. This review aims to provide an overview of the current state of research and identify research gaps and future directions in this emerging field. The review follows the PRISMA-ScR guidelines and includes eligible publications published between 2017 and July 2023. Four electronic databases were searched and 41 of the 224 initial papers were eventually selected for review. The findings reveal key terms related to GenAI in language education, the most researched language study and education levels, areas of research, attitudes towards GenAI, and the potential benefits and challenges of GenAI application. The review highlights several research gaps, including the need for more empirical studies to assess the effectiveness and impact of GenAI tools, discussion of ethical considerations, targeted interventions for specific language skills, and stakeholder engagement in responsible integration. Educators are encouraged to incorporate GenAI tools into their teaching practices while remaining vigilant about potential risks. Continuous professional development for educators is crucial to ensure informed decision-making and effective integration of GenAI tools. This scoping review contributes to the existing knowledge on the use of GenAI in language education and informs future research and practice in this disruptive and rapidly evolving field.

Making a Better Logistic Regression Model Using Ensemble Learning

Abstract: Machine learning is a hot research topic in today's society and an important direction in the field of image classification research. Image classification is a supervised learning method used to classify images. There are many image classification algorithms that perform differently in different conditions. Paper focuses on improving the performance of image classification algorithms by using ensemble learning. Paper analyses the performance of logistic regression model and support vector machine model and then uses ensemble learning to increase the performance of logistic regression model. In the research work, both theoretical and empirical approaches were followed. For the theoretical approach a review of both secondary data as well as data based on results obtained by application on the tools is studied. Secondary data was acquired from the research articles, text books, journals, technical reports, published thesis, websites, e-journals, software tool manuals, conference proceedings and any other research articles published in the related domain. The empirical study was carried out on the set of experiments, using software tools. The results obtained from the experiments were analyzed for the finding of the research. The paper compares the results of three algorithms when tested on same dataset, in same environment and on same system. The final results of the research prove that ensemble techniques give best results and this type of learning is effective.

Impact of Stress on the Sleep Pattern and Cognitive Performance on Young Adults

Abstract: This research paper investigates the intricate relationship between stress, sleep patterns, and cognitive performance among young adults. In today's world, stress is a common occurrence that has a significant impact on people's physical and mental health as well as their cognitive abilities. The purpose of this study is to clarify how stress affects young people' sleep patterns and cognitive function, as they are a group that is more susceptible to changes in sleep and cognition caused by stress. The study looks at how stress affects many areas of cognitive performance, including attention, memory, problem-solving, and decision-making, as well as the length, quality, and architecture of sleep. It does this by drawing on current literature and empirical studies. The study also examines possible mechanisms, such as neuroendocrine reactions, changes in brain connections, and psychological elements like perceived control and coping mechanisms, that may underlie the observed effects of stress on sleep and cognition. Furthermore, the paper discusses the bidirectional relationship between stress, sleep, and cognitive performance, highlighting the complex interplay between these domains and their cumulative impact on young adults' overall well-being. The findings of this study have implications for public health campaigns, clinical practices, and preventative treatments targeted at reducing the negative effects of stress on sleep and cognition, building resilience, and encouraging young adults to operate cognitively at their best

Impact of technological innovations on healthcare delivery: A literature review of efficiency, patient care, and operational challenges

The rapid integration of technology in healthcare systems globally has ushered in transformative changes in patient care, operational efficiency, and data management. This literature review assesses the impact of emerging technologies such as blockchain, information technology, electronic medical records (EMRs), and artificial intelligence (AI) within healthcare settings. Drawing from a wide range of peer-reviewed articles and empirical studies, this paper synthesizes current research findings to understand the multifaceted effects of these technologies on healthcare delivery. The review highlights how these innovations enhance healthcare quality, streamline operations, and tackle prevalent challenges, particularly focusing on their implementation in Saudi Arabian healthcare contexts. Key findings indicate that while technological advancements offer significant improvements in patient care and system management, they also introduce challenges related to privacy, security, and the need for significant infrastructure investments. The review underscores the necessity for ongoing research to navigate technology adoption’s complexities and optimize healthcare practices effectively. This paper contributes to academic and practical discussions on leveraging technology to foster more efficient and responsive healthcare systems worldwide.\

Human resource strategies for resilient supply chains in logistics and transportation: A critical review

This study systematically reviews and analyzes the literature on Human Resource Management (HRM) strategies for enhancing supply chain resilience in the logistics and transportation sector, identifying the pivotal role HRM plays in fortifying supply chains against disruptions. Utilizing a systematic literature review and content analysis, this research examines articles from peer-reviewed journals, focusing on publications from 2004 onwards to capture contemporary HRM practices and their impact on supply chain resilience. The methodology involves a detailed search strategy across multiple databases, employing specific inclusion and exclusion criteria to ensure the relevance and quality of the literature reviewed. Key findings reveal that workforce agility, technological integration in HRM practices, leadership development, and the cultivation of a resilient organizational culture are essential components of effective HRM strategies in this context. These elements collectively contribute to the resilience of supply chains, enabling organizations to maintain operational continuity during disruptions. Based on these insights, the study proposes strategic recommendations for industry leaders and policymakers, emphasizing the importance of investing in employee development, adopting technology-enhanced HRM practices, and fostering ethical and sustainable HR practices to support supply chain resilience. Concluding, the study highlights the need for further research, particularly empirical studies that examine the direct impact of HR strategies on supply chain performance. This research underscores the critical role of strategic HRM in enhancing supply chain resilience, offering a roadmap for future efforts in the logistics and transportation sector.

Examining Citizens’ Expectations and Perceptions About Service Quality in Public Sector: Adapting a Hybrid Public Service Quality Model to the Local Context

ABSTRACT The empirical study examines citizens’ expectations and perceptions about service quality in three major public institutions. The article adopts a quantitative approach. Citizens’ expectations and perceptions about service quality in the context of public institutions were examined through a questionnaire, utilizing a hybrid model of SERVQUAL with private and public service quality dimensions. The article presents the results of a quantitative study (sample of 310 citizens), distributed across three public institutions, illustrating factor, gap, paired correlation and one-way analysis of variance (ANOVA) of survey data. For factor extraction, rotation and interpretation, the Oblimin rotation technique was used, determining inter-correlation among the public service quality factors. As for ANOVA analysis, Levene’s test was used to verify the homogeneity of variance assumption (the variance in scores for each group of customers). The findings of the study suggest the re-distribution of components under the refined service quality factors. Different items of initial service quality dimensions are rotated under newly created factors. Gap analysis reveals no variations in terms of citizens’ expectations across public institutions, but suggests significant differences among public entities in regard to perceptions of services acquired. Adapted Hybrid Public Service Quality model will assist public institutions in developing and tailoring service quality management systems to citizens’ needs. This is the first attempt to carry out a quantitative approach to researching Service Quality in public institutions. The primary contribution of the study is that it has tested a hybrid model for measuring perceived public service quality, integrating service quality dimensions both from public and private sectors and proposed a refined model fit for the local context considering citizens’ characteristics and the current state of the public sector experiencing a shift toward modernization of services.

Sequential Bayesian Phylogenetic Inference.

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously confident results. Here, we formally develop and test a sequential inference approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior distribution produced in the previous step. Our sequential inference approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in software choice (and hence model availability) and can be computationally more efficient than the traditional joint inference approach when multiple models are being tested. We show that our sequential inference approach is identical in practice to the joint inference approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many importance samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, e.g., a single phylogeny estimate to transform an unrooted phylogeny into a time-calibrated phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of divergence-time estimation in insects using a relaxed clock model from transcriptome data. In the empirical example, we estimate three posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Γ+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply three different node-calibration strategies and show that divergence-time estimates are affected by both the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approaches for divergence time estimation, including branch-length estimation from other software.

Board diversity, female executives and stock liquidity: evidence from opposing cycles in the USA

Purpose This study aims to address gaps and limitations in the literature on corporate governance and stock liquidity. It explores the potential benefits of increasing female representation in corporate leadership, which has been a subject of debate and policy intervention in recent years. Design/methodology/approach Based on prior empirical studies and by integrating the insights of different theories, this study links gender diversity to stock liquidity and uses a multivariate panel regression approach. Findings The results show that gender diversity, both on the board and in executive positions, positively and consistently affects stock liquidity across different business cycles. The findings reinforce the notion that diverse executive leadership is crucial and influential irrespective of the prevailing economic conditions. Practical implications This study has practical implications for investors, managers and policymakers who are interested in the benefits of gender diversity in corporate leadership. It suggests that increasing the percentage of female executives and board members can improve stock market liquidity, which is a key indicator of market efficiency and firm value. Social implications This study advocates for gender equality and diversity in corporate leadership, which can benefit society. It demonstrates that the presence of women directors can enhance financial stability and thus benefit the stakeholders and the community. Originality/value This study contributes to the academic literature by examining the impact of gender diversity on board and executive levels on stock liquidity in the US market. Previous research on this topic has mainly relied on French or Australian data. Moreover, this study extends previous work through examining the case of executives’ gender diversity. To the best of the authors’ knowledge, this study is the first to analyze the relationship between gender diversity and stock liquidity across different business cycles, providing a nuanced understanding of how economic contexts affect this relationship.

Beyond breathing exercises: rethinking mindfulness through a Buddhist lens to combat unethical decision-making in organizations

Purpose The purpose of this study is to explore the intersection of mindfulness practices and ethical decision-making within organisational leadership. Drawing from ancient Buddhist principles and contemporary neuroscience, this study aims to illuminate how mindfulness can enhance cognitive and emotional regulation, thereby fostering ethical behaviour and improved decision-making among leaders and employees. By examining the theoretical and practical implications of mindfulness in the context of organisational behaviour, this research seeks to contribute to the development of more compassionate, ethical and effective leadership practices, ultimately promoting a more mindful and sustainable business environment. Design/methodology/approach This concept paper explores the integration of mindfulness meditation practices with decision-making, particularly its influence on ethical choices, through a comparative study of modern techniques and the ancient teachings of the Tripitaka. Using a methodology that spans literature review in organisational behaviour and leadership, alongside in-depth analysis of the Tripitaka and contributions from scholars like Bhikkhu Bodhi, the paper examines the potential of mindfulness in enhancing ethical decision-making. It incorporates a range of sources, including peer-reviewed journals and seminal books across various disciplines, to underscore the transformative potential of mindfulness in addressing contemporary challenges and guiding leadership practices. Findings This discussion explores how mindfulness, rooted in ancient Buddhist philosophy and aligned with modern neuroscience, can significantly enhance managerial decision-making by fostering a balance between cognitive and emotional factors. It delves into the transformative potential of mindfulness in refining thought processes, promoting ethical decision-making and mitigating cognitive biases. By bridging traditional wisdom with contemporary scientific insights, the analysis underscores mindfulness as an active, dynamic process crucial for personal growth and effective leadership in complex environments. Research limitations/implications One limitation of this research is its reliance on theoretical frameworks and literature reviews, which may not capture the full range of practical challenges in implementing mindfulness practices within organisations. Additionally, the diversity in mindfulness methodologies and the subjective nature of mindfulness experiences may affect the generalisability of the findings. Future research should include empirical studies to validate the proposed benefits of mindfulness in organisational settings and explore the most effective strategies for integrating mindfulness practices into leadership and decision-making processes. This would help in understanding how mindfulness can be tailored to suit different organisational cultures and individual preferences. Practical implications The practical implications of applying mindfulness in organisational settings include enhanced decision-making abilities, improved leadership effectiveness and increased employee well-being. Mindfulness training can equip leaders and employees with the skills to manage stress, navigate complex ethical decisions and maintain focus amidst distractions, leading to more thoughtful and responsible business practices. Organisations might see a reduction in conflict, enhanced creativity and better teamwork, contributing to a more harmonious and productive workplace. Implementing mindfulness programmes could also support talent retention and attraction by promoting a workplace culture that values mental health and ethical behaviour. Social implications The social implications of integrating mindfulness into organisational decision-making and leadership, as suggested by the document, include promoting ethical behaviour, enhancing emotional regulation and improving team dynamics. Mindfulness practices can lead to more informed and conscious decision-making, reducing cognitive biases and fostering a culture of ethical awareness within organisations. This shift towards mindful leadership could potentially transform organisational cultures, encouraging greater compassion, ethical responsibility and collective well-being, thereby contributing positively to broader societal values and norms. Originality/value The originality and value of this research lie in its novel integration of mindfulness concepts derived from ancient Buddhist teachings with contemporary neuroscience and organisational behaviour studies. By exploring the deep-rooted philosophical underpinnings of mindfulness and their applicability to modern ethical decision-making and leadership practices, this work offers a unique perspective that bridges historical wisdom with current scientific understanding. It provides a comprehensive framework for understanding the transformative potential of mindfulness in organisational settings, highlighting its capacity to foster ethical leadership, enhance decision-making processes and contribute to a more mindful, compassionate and sustainable business environment.

Behavioral traits of fund managers: a systematic literature review

PurposeThe purpose of the study is to investigate, synthesize and critically evaluate empirical research findings on the behavioral traits of fund managers from 1994 to 2024. The ultimate goal is to provide a unified body of literature on three broad topics: first, fund managers' demographic and professional characteristics, such as age, gender, level of education and years of industry experience; second, fund managers' social and political connections; and third, fund managers' behavioral biases that lead to irrational investment decisions.Design/methodology/approachThe relevant papers from selected journals were discovered and manually validated using the Scopus database. From 317 retrieved documents, 57 relevant articles were chosen and analyzed after the forward and backward search of the existing articles.FindingsThis paper presents a categorized summary of behavioral factors that have gained a foothold in influencing the behavior of fund managers in fund management research, with several studies demonstrating their significance leading to improved prediction and model precision, as this review indicates. In addition, the study summarized the contributions of prior empirical studies within the aforementioned three major categories and illustrated their consequences.Originality/valueThe present study contributes to the understanding of the effects of behavioral finance theories on fund managers by providing meaningful explanations of their behavioral traits based on empirical evidence and existing trends and knowledge gaps, both of which can influence the future direction of research.

Scaling of social initiatives: the role of entrepreneurial skills and positions

PurposeThe scaling of social initiatives is important to achieve broad social impact based on successful small-scale experiments. This paper focuses on the influence of the characteristics of the initiators of social initiatives on scaling processes. The limited literature on this topic highlights two critical actor characteristics: high entrepreneurial skills and a central position in the area. Both characteristics influence two critical components of the scaling process: mobilizing stakeholders and focusing on retaining effectiveness. The purpose of this paper is to explore these complex relationships in a deductive analysis and to use these findings for an inductive analysis to generate new insights and extend our academic understanding.Design/methodology/approachA comparative qualitative study of 20 social initiatives in the Dutch social sector was conducted, including 48 in-depth interviews with initiators and stakeholders in three different areas – mental health, debt and labour participation.FindingsHigh entrepreneurial skills are more important for mobilizing stakeholders and focusing on retention of effectiveness than the position of the initiators, but these are a condition rather than a guarantee. Creating space for scaling and investing in measuring effectiveness in other contexts are also important.Originality/valueBy combining the literature on social entrepreneurship and public innovation and conducting an empirical study, our study provides a broad and nuanced picture and brings precision to our understanding of the relationships between initiators' entrepreneurial skills and position and the scaling process.

The impact of fake online reviews on customer satisfaction: an empirical study on JD.com

The impact of fake online reviews on customer satisfaction: an empirical study on JD.com