Data Analysis Methodology Research Articles

UNMASKING DECEPTION: FORENSIC ACCOUNTING'S INFLUENCE ON FRAUD REDUCTION IN INDIA'S BANKING INDUSTRY

The significance of forensic accounting in fraud reduction cannot be overstated, particularly in a complex financial landscape like India's banking industry. Forensic accounting serves as a crucial instrument in detecting, investigating, and preventing fraudulent activities that pose substantial threats to the integrity and stability of financial institutions. Through specialized techniques like data analysis, financial examination, and investigative methodologies, forensic accountants play a pivotal role in uncovering fraudulent schemes, tracing illicit financial transactions, and providing evidence for legal proceedings. This study delves into the critical role of forensic accounting in combating fraudulent activities within India's banking sector. With a focus on enhancing understanding and awareness, the research sets out three primary objectives. Firstly, it aims to investigate the current landscape of forensic accounting in India, exploring its methodologies, practices, and regulatory framework. Secondly, the study seeks to assess the level of awareness among bank managers regarding the efficacy of forensic accounting in reducing instances of fraud. Finally, it endeavours to analyse the relationship between the demographic profiles of bank managers—such as age, gender, and years of experience—and their awareness of forensic accounting's role in fraud reduction. Moreover, the findings of this study carry practical implications for various stakeholders, including policymakers, regulators, banking professionals, and industry participants. By gaining insights into the awareness levels of bank managers regarding forensic accounting and its efficacy in fraud reduction, policymakers and regulators can develop targeted interventions to enhance training, education, and awareness initiatives within the banking industry. The study's methodology involved interviews with 200 bank managers, allowing for a comprehensive assessment of the impact of forensic accounting on fraud reduction within India's banking industry.

The Effect of Workload and Perception of Organizational Support on Job Satisfaction with Work-Life Balance as A Mediating Variable in the Shared Services Function of Pt Pertamina (PERSERO)

The aim of this research is to determine how workload and the perception of organizational support impact job satisfaction, as well as how work-life balance acts as a mediating variable in the Shared Services Function of PT Pertamina (Persero). The research strategy employed by the author is quantitative in character, as it is grounded in concrete, objective, quantifiable, logical, and methodical data. Because it contains samples from every permanent employee of PT Pertamina (Persero) as well as from the whole population of the Shared Services Function, the study's sample is known as a saturated sample, or full population. 321 individuals make up the entire population sample. Structural Equation Modelling (SEM) is a data analysis methodology that was employed in this work to solve the limitations of the regression method. The Partial Least Square (PLS) software application is used in this study's analysis to process the data. Variant Based Structural Equation Modelling is then used to test the hypothesis and provide a viable model. The results showed that workload had a negative and significant impact on job satisfaction in the SS Function of PT. Pertamina (Persero). Job satisfaction with SS Function PT. Pertamina (Persero) is positively and significantly impacted by employee perceptions of organizational support. In SS PT Function Pertamina (Persero), work-life balance is adversely and significantly affected by workload. The perception of organizational support has a favorable and considerable impact on work-life balance, as per the SS function of PT. Pertamina (Persero). Work-life balance and job satisfaction have a somewhat positive association in SS Function PT. Pertamina (Persero). Work-life balance makes it possible for employee happiness at PT. Pertamina (Persero), an SS function, to be indirectly impacted by workload. The SS function PT. Pertamina (Persero) believes that work-life balance has an indirect effect on satisfaction through organizational support.

Advances in Small Angle Neutron Scattering on Polysaccharide Materials.

Polysaccharide materials and biomaterials gain the focus of intense research owing to their great versatility in chemical structures and modification possibilities, as well as their biocompatibility, degradability, and sustainability features. This review focuses on the recent advances in the application of SANS on polysaccharide systems covering a broad range of materials such as nanoparticulate assemblies, hydrogels, nanocomposites, and plant-originating nanostructured systems. It motivates the use of SANS in its full potential by demonstrating the features of contrast variation and contrast matching methods and by reporting the methodologies for data analysis and interpretation. As these soft matter systems may be organized in multiple length scales depending on the interactions and chemical bonds between their components, SANS offers exceptional and unique opportunities for advanced characterization and optimization of new nanostructured polysaccharide materials.

Development of Project Based Learning Model in Object Oriented Programming (OOP) Learning in Polytechnic

This research is to investigate the design, validity, practicality, and effectiveness of the Project Based Learning Model in the context of Object Oriented Programming Learning at Polytechnic. This study is categorised as research and development (R&D), employing a learning methodology that is derived from the ADDIE paradigm (Analysis, Design, Develop, Implementation, and Evaluation). Methods of data collection including the utilisation of observation and questionnaires. The data analysis methodologies employed include validity analysis, reliability analysis, practicality analysis, and effectiveness analysis. The study yielded the following results: (1) the Project Based Learning Model in Object Oriented Programming Learning encompasses the design of a Learning Model, the identification of Course Learning Outcomes (CPMK), the determination of Learning Outcome Indicators, the selection of Learning Materials, the development of Semester Learning Plans (RPS), the creation of Learning Modules, the formulation of Student Task Plans (RTM), and the establishment of Learning Outcome Assessment Rubrics. (2) The Project Based Learning Model in Object Oriented Programming Learning has a validity of 0.84, meeting the criteria for being highly valid. (3) The practicality of implementing the Project Based Learning Model in Object Oriented Programming Learning is rated at 3.38, falling under the very practical category. Additionally, the lecturer's responses to this learning model are also highly practical, with a rating of 3.44. (4) The effectiveness of the Project Based Learning Model in Object Oriented Programming Learning is rated as high based on student learning test results. Furthermore, students' responses to this learning model are highly effective, with a rating of 3.31. Hence, it can be inferred that the Project Based Learning Model in Object Oriented Programming (OOP) Learning fulfils the requirements of being legitimate, practical, and efficacious for usage at the Polytechnic.

The information-seeking behaviors of project managers in professional virtual communities

Purpose This research explores project manager (PM) behavior in their professional virtual communities (PVCs), using social identity theory as a theoretical foundation. The purpose is to examine the extent to which PMs seek information on key topics in the Project Management Body of Knowledge Guide (PMBoK).Design/methodology/approach A text data analytics methodology that uses quantitative and qualitative analysis techniques is followed. The research method reveals relationships in language-based data gathered from six project management forums and blogs.Findings Information related to all the PMBoK topics is sought in the project management virtual communities. People management topics account for a dominant portion of interactions. The findings enhance social identification theorizing for the PM role. From a practical standpoint, the findings shed light on focal areas for greater emphasis in PM PVCs.Originality/value Our people management finding constructively replicates existing findings via a large, global sample and strengthens calls for increased focus on people management matters in project management. As a result, we call for increased scholarly attention to people management in project management. Finally, we encourage pursuit of several research questions to enhance knowledge of PM information-seeking behavior.

Improving Lecturer Performance Through Organizational Culture, Serving Leadership, Personality, And Work Motivation

This research aims to find strategies to improve performance by analyzing the influence of organizational culture, servant leadership, personality, and work motivation. The research method used is quantitative. The sample consisted of 124 people from a population of 178 spread across twelve study programs using the proportional random sampling method. Path analysis and SITOREM are used in data analysis methodologies. Based on the analysis’s results, it was concluded that a) Organizational culture directly affects performance by 19.9%; b) Servant leadership directly affects performance by 14.4%; c) Personality directly affects performance by 10.6%; d) Work motivation directly affects performance by 6.9%; e) Organizational culture directly affects motivation at work by 23.1%; f) Servant leadership directly affects workplace motivation by 40.3%; g) Personality does not directly affect workplace motivation by 25.1%; h) Organizational culture has a 1.6% indirect impact on performance through work motivation; i) Servant leadership has a 2.8% indirect impact on performance through work motivation; and j) Personality has a 1.7% indirect impact on performance through work motivation.

Analysis Of The Influence Of Work Motivation, Work Environment And Job Stress On Employee Performance Of National Food Retail Companies

The purpose of this study is to examine the potential effects of work environment, motivation, and stress on employee performance. All of the participants in this study are employees. The saturation sampling strategy is employed in this study. The study's sample comprised one hundred participants. In this study, the methods of gathering data were observation and questionnaires. To make inferences about the issue being researched, the data gathered from the completed questionnaire is subsequently processed and examined with the aid of data processing techniques. Multiple regression analysis, descriptive testing, classical assumption testing, and data quality testing are all used in this study's data analysis methodology. The following findings are derived from the data that was gathered and examined using a multiple linear regression model: Employee performance is influenced by the workplace. Employee performance is influenced by work motivation. The impact of job stress on employee performance is zero. Workplace motivation, work atmosphere, and job stress all affect how well employees perform.

Belonging in the workplace: Methodology for fair and equitable data analysis

ABSTRACT To remain globally competitive, the Canadian mining industry requires sustainability protocols to enhance the hiring and retention of diverse and underrepresented employees. Belonging in the workplace acts as a bridge, but literature demonstrates bias in current survey analysis practices that reinforces status quo and favors homogeneous groups. Using mediation analysis, this research investigated how an employee’s intersections of identity (gender, ethnicity, and career level) influence belonging in the workplace perception. Data from 3,508 participants from 13 Toronto Stock Exchange listed companies were used to evaluate perceived organizational belonging through five validated indicators (comfort, connection, contribution, psychological safety, and well-being). Using multiplicative analysis, we explored how employees’ intersecting identities change their perception of belonging in the workplace. Study results show clear direct and indirect effects when intersections of identity are accounted for. With the intersections of identity frequently misunderstood in survey analysis and the workplace, this research explores how status quo decisions lead to exclusion and turnover of underrepresented employees. Applying mediation analysis explains the variance in perception of belonging in the workplace and provides insight into the distortions of workplace experience while providing support for sustainability protocols.

Preferred terminology of people with serious mental illness.

People with serious mental illnesses (SMIs) have argued for name changes for their conditions given problems with oversimplification, stigma, and social exclusion. There is a need to better understand the terminology preferences of people with SMI. The present two-part qualitative study analyzed data regarding participant preferences and evaluations of different labels for SMIs using qualitative data analysis methodologies of grounded theory and content analysis. The range of names identified by people with SMI in the present qualitative interviews and the positive and negative evaluations of the different labels are presented. Results were integrated across Study 1 and Study 2 to identify the following preferred and nonpreferred terms for these mental health problems; mental illness, diagnosis, disorder, psychiatric disability, person in recovery, person with lived experience, mental health challenges/issues, chemical imbalance, symptom names, and self-constructed labels. These results suggest the need to identify and reflect the terminology preferences of each individual with mental health problems when seeking psychological services and recognize the varying benefits of different terms depending on time and context. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Does Green Bond Issuance Enhance Market Return of Equity Shares in the Indian Stock Market?*

AbstractThis study examines the announcement effect of a green bond on the issuer's stock returns in the Indian Stock Market. The event study methodology for data analysis and abnormal returns were calculated using the market model for the 16‐day event window that includes the 5 days prior and 10 days after the issuance of the green bonds. The findings of this study show that green bond announcements do not create any significant abnormal returns, thus suggesting investors' irrationality toward environmental factors. This study informs policymakers that investor education relating to the environment is needed in India.

Peran Strategi Pembelajaran dan Implementasinya Pada Era Digital

In the digital age, effective learning strategies not only focus on delivering material, but also involve students actively in the learning process, encourage deep understanding, and develop skills relevant to the digital world. In addition to the role of learning strategies, their implementation is also key in ensuring learning success in this digital era. The objective of this research is to explore the importance of learning strategies and their application in the digital era. To achieve this, a qualitative research approach is employed with data being sourced from a range of relevant literature. The Miles and Huberman model is utilized as the data analysis methodology, encompassing process of data reduction, data presentation, and conclusion drawing. The role of learning strategies in digital era is supporting factors that help increase student learning motivation, achieve learning goals, and update teacher teaching skills. The implementation of learning strategies in the current digital era are 1) Learning models such as blended learning, distance learning, mobile learning and virtual learning environment. 2) Learning innovations such as google sites, whatsapp, teamviewer, remote utilities, aeroadmin, google drive and google forms. 3) Digital learning design developed by applying the principles of independence, flexibility, contemporary, mobility and suitability to the level of need.

A Tutorial on Bayesian Linear Regression with Compositional Predictors Using JAGS

This tutorial offers an exploration of advanced Bayesian methodologies for compositional data analysis, specifically the Bayesian Lasso and Bayesian Spike-and-Slab Lasso (SSL) techniques. Our focus is on a novel Bayesian methodology that integrates Lasso and SSL priors, enhancing both parameter estimation and variable selection for linear regression with compositional predictors. The tutorial is structured to streamline the learning process, breaking down complex analyses into a series of straightforward steps. We demonstrate these methods using R and JAGS, employing simulated datasets to illustrate key concepts. Our objective is to provide a clear and comprehensive understanding of these sophisticated Bayesian techniques, preparing readers to adeptly navigate and apply these methods in their own compositional data analysis endeavors.

THE SOUNDNESS LEVEL ASSESSMENT OF PT BANK PAN INDONESIA TBK USING RISK-BASED BANK RATING IN 2018-2022

The financial industry is crucial for a nation's growth and well-being, with banks playing a vital role in utilizing financial resources. The soundness of a bank is essential, with stakeholders including shareholders, bank management, customers, and the general public involved. Maintaining soundness is paramount for financial institutions like banks, as internal risk can affect the bank's financial status. This study examines the financial report of PT Bank Pan Indonesia Tbk for 2018-2022. The research uses historical data and descriptive data analysis methodology, analyzing financial statements using the Risk-Based Bank Rating method. This study indicates that PaninBank's soundness level is in a Very Healthy condition from 2018 to 2022, as measured by the NPL ratio, Good Corporate Governance, ROA ratio, NIM ratio, BOPO ratio, and CAR ratio. The soundness level of the PaninBank is also in a Very Healthy condition, with composite scores of 93%, 93%, 93%, 90%, and 93% for the five years spanning 2018-2022.

A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs

Graph mining has emerged as a significant field of research with applications spanning multiple domains, including marketing, corruption analysis, business, and politics. The exploration of knowledge within graphs has garnered considerable attention due to the exponential growth of graph-modeled data and its potential in applications where data relationships are a crucial component, and potentially being even more important than the data themselves. However, the increasing use of graphs for data storing and modeling presents unique challenges that have prompted advancements in graph mining algorithms, data modeling and storage, query languages for graph databases, and data visualization techniques. Despite there being various methodologies for data analysis, they predominantly focus on structured data and may not be optimally suited for highly connected data. Accordingly, this work introduces a novel methodology specifically tailored for knowledge discovery in labeled and heterogeneous graphs (KDG), and it presents three case studies demonstrating its successful application in addressing various challenges across different application domains.

A novel methodology for data analysis of dynamic angle of repose tests and powder flow classification

We present a new post-processing methodology to analyse powder flow image data gathered via dynamic angle of repose tests. We aim to expand the flow descriptors, allowing a more detailed and nuanced measurement of flow rheology. This makes the data extraction reliable even if the free surface profile is not clearly identifiable. After defining 30 flow descriptors to be measured from powder flow snapshots, we use Principal Component Analysis to understand their relations with the physics of the system. The set of descriptors is optimised, and the most significant ones are identified, allowing the physics to be captured with fewer essential parameters. We demonstrate that a comprehensive picture of powder flow is achievable simply with the centre of mass and the flow merging point. Our research demonstrates that traditional data extraction methodologies are insufficient to fully describe the flow, making our framework ideal for enhancing the understanding of flow properties.

Seas of Motivation: OCEANTG.COM in Maritime English Training

Maritim education possesses a pivotal purpose in endowing kadet with competencies that enable it to function efficiently in the increasingly complex global maritime discourse. This study aims to assess the extent to which the motivation mentioned above can be transmitted through the use of OCEANTG.COM in the study of Maritime English (ME). The methodology for data analysis involved the utilization of a quantitative research design incorporating a questionnaire that was modified from Gardner's Attitude/Motivation Test Battery. The paired t-test yielded significant results regarding the deficient motivation variable with a significance level (Sig.) of 0.000, thus indicating substantial evidence. OPEN SEATNICH.COM, a platform for learning, menadakan potential as a source of inspiration that encourages individuals to comprehend and apply knowledge in the field of maritime English. OCEANTG.COM served as a valuable resource for the development of a practical curriculum for marine students due to its interactive content, fungsional inklusif, and adherence to industry standards. This thesis asserts that there is no need for an innovative thesis statement regarding the motivation of a junior in maritime English.

Comparative Analysis of Time Series Prediction Algorithms on Multiple Network Function Data of NWDAF

With the emergence and vigorous development of 5G technology, there is a significant surge in network usage and traffic, resulting in heightened complexity within network and IT environments. This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). This function grants network operators the flexibility to either employ their own data analytics methodologies based on machine learning (ML) and deep learning (DL) into their networks. In this paper, we present a dataset named “NWDAF-NFPP” for network function performance time series prediction, collected from a laboratory at China Telecom. The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. The availability of this dataset provides valuable resources for researchers to conduct time series prediction research on network element performance. Following data collection, a total of six models were employed for network element performance prediction, encompassing both machine learning and deep learning approaches. This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. This comparative analysis will provide valuable insights into the strengths and limitations of each model, aiding in informed decision-making for network optimization and management strategies in the future.

STASISM: A Versatile Serious Gaming Multi-Sensor Platform for Personalized Telerehabilitation and Telemonitoring.

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a sensor-based telerehabilitation and telemonitoring system, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for the remote monitoring of a patient's progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings.

Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data.

Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a 5-year period were obtained and reinterpreted utilizing the previously validated Franklin© Artificial Intelligence (AI). Using PowerBI©, the data were further matched to patients in the electronic healthcare record to associate with demographic data to generate a variant data table and mapped by ZIP codes. Three thousand and sixty-five variants were identified and 98% were matched to patients with geographic data. Franklin© changed the interpretation for 24% of variants. One hundred and fifty-six clinically actionable variant reinterpretations were made. A total of 739 Mendelian genetic disorders were identified with disorder prevalence estimation. Mapping of variants demonstrated hot-spots for pathogenic genetic variation such as PEX6-associated Zellweger Spectrum Disorder. Seven patients were identified with Bardet-Biedl syndrome and seven patients with Rett syndrome amenable to newly FDA-approved therapeutics. Utilizing readily available software we developed a database and Exploratory Data Analysis (EDA) methodology enabling us to systematically reinterpret variants, estimate variant prevalence, identify conditions amenable to new treatments, and localize geographies enriched for pathogenic variants.

Public Perception Toward the Malaysian National COVID-19 Immunisation Programme (PICK) in the State of Sabah, Malaysia: A Cross-Sectional Survey.

The Malaysian Government has initiated the National COVID-19 Immunisation Programme, known as PICK, to be a national strategy for addressing the spread of the coronavirus disease (COVID-19) pandemic across the country. Although the government intensified public awareness to increase program registration, the total number that registered in the state of Sabah, located in East Malaysia, was relatively low during August 2021, accounting for only 42.9% as compared to that of Peninsular Malaysia. Therefore, this paper examines the public perception toward the PICK program in Sabah based on 4 main components: safety, communication, psychology, and milieu. This study is based on the empirical findings drawn from 1024 respondents across Sabah using online Google Form surveys. This study adopts 5 methodologies for data analysis by using K-means clustering, mean score, Mann-Whitney U test, spatial analysis, and frequency analysis. It has been revealed that the percentage of respondents (categorized as Cluster 1) who have a negative perception toward the vaccination program is higher (55.9%) than those who have a positive perception (44.1%). This study further discovered that Cluster 1 has shown high skepticism regarding the vaccination program, which can be explained through the communication component (M = 3.33, SD = 0.588), especially Co2, Co3, Co1, and Co4. Following the communication factor, a chain of negative perceptions also affects other components such as safety, psychology, and milieu among Cluster 1, all of which contribute to poor participation in the PICK program. The study outcomes are extremely useful for informing local authorities to establish policies related to public interests, primarily in the areas of public health. Understanding the community's perspectives and their obstacles in participating in such programs may assist local authorities in developing or implementing public policies and campaigns that ensure such related public programs can be conducted more effectively in the future.