Digital Landscape Research Articles

Navigating the Integration of Blockchain Technology in Banking: Opportunities and Challenges

Integrating blockchain technology within the banking sector represents a pivotal transformation, promising enhanced security, transparency, and efficiency in financial operations. This qualitative research employs a systematic review approach to investigate the current state, challenges, and prospects of blockchain adoption in banking, drawing insights from a comprehensive analysis of scholarly literature. Through a systematic search and review of relevant academic journals, conference papers, books, and reports, this study explores critical variables such as security, transparency, efficiency, challenges, opportunities, and impact associated with blockchain adoption in banking. Thematic analysis is the primary methodological approach to identify recurring themes, patterns, and relationships across literature, facilitating a nuanced understanding of blockchain's implications for banking operations. The findings reveal a dual nature of blockchain adoption in banking, highlighting significant opportunities alongside critical challenges. While blockchain technology offers enhanced security, transparency, and efficiency, challenges like scalability issues, regulatory uncertainties, and interoperability concerns persist. Nonetheless, the study underscores the transformative potential of blockchain in revolutionizing banking operations, urging policymakers and banking practitioners to address these challenges through continued research, technological innovation, and collaborative efforts. By leveraging blockchain effectively, the banking industry can navigate the digital landscape, enhance operational efficiency, and deliver superior customer experiences, thereby gaining a competitive edge in the financial sector

Navigating the Digital Landscape: Social Media and Employee Privacy in Nepalese Service Sector

Navigating the Digital Landscape: Social Media and Employee Privacy in Nepalese Service Sector

State Platform Capitalism: The Geopolitical Dynamics of Sustainable Digital Power in the U.S. and China

In the contemporary digital landscape, digital power has emerged as a critical factor influencing global economic and geopolitical dynamics. This study examines the interplay between government regulations and technological innovation in shaping sustainable digital power, focusing on the United States and China. Leveraging the theory of state capitalism, the research integrates these variables into a comprehensive framework to explore their combined impact. Through a detailed literature review, the study highlights the importance of effective regulatory frameworks and continuous technological advancements for enhancing sustainable digital power. Using a robust methodological approach, the findings reveal significant positive relationships between government regulations, technological innovation, and sustainable digital power. The study underscores the necessity of a balanced approach to regulation and innovation to maintain a competitive edge and secure national interests. The contributions of this research are vital for policymakers and industry stakeholders, providing insights into how regulatory policies and technological investments can be optimized to enhance sustainable digital capabilities. This study fills a critical gap in the literature by offering a holistic understanding of digital power dynamics and its implications for global and national strategies. The paper concludes with practical recommendations and directions for future research.

Process for Improving the Quality of Digital Literacy Education for Bankers in Timor Leste

Digital literacy is crucial for economic and social development, particularly in regions like Timor Leste aiming to enhance educational standards among professionals such as bankers. This study aims to investigate the process of improving digital literacy education for bankers in Timor Leste, focusing on enhancing their skills in using digital tools and platforms effectively. The research adopts a qualitative approach, using interviews and surveys to gather insights from bankers regarding their current digital literacy levels and educational needs. Data analysis will involve thematic analysis to identify key areas for improvement. Initial findings indicate a significant gap in digital literacy among bankers in Timor Leste, highlighting a need for tailored educational programs. Recommendations include the development of specialized training modules focusing on digital security, data analytics, and digital communication tools. In conclusion, enhancing digital literacy among bankers in Timor Leste is essential for their professional growth and the economic development of the region. Effective educational interventions can empower bankers with the necessary skills to navigate the digital landscape competently and contribute to sustainable development goals.

Self-Adaptable Software for Pre-Programmed Internet Tasks: Enhancing Reliability and Efficiency

In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software’s effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation.

Unveiling Digital Leadership: Exploring Models and Theories

Digital leadership has emerged as an essential strategy in addressing the demands of the digital era. This study reviews academic research on digital leadership models and theories, drawing from published articles in the field. This study has synthesized existing research to create a robust framework that integrates transformational and distributed leadership principles. The framework clarifies the essential components of effective digital leadership and provides practical guidance for organizations navigating the digital landscape. By combining insights from various leadership theories with current digital practices, this research improves our understanding of leveraging technology for organizational success. Moving ahead, it is crucial to empirically validate and apply this framework to verify its effectiveness and adaptability in diverse contexts. The research findings lay the foundation for further exploration of digital leadership strategies, enabling sustainable digital transformation in organizations. The proposed model presents opportunities for future research to explore the broader impact of digital leadership on digital transformation.

Methods of Extracting and Analyzing Metadata for Evidentiary Purposes

This paper examines methods for extracting and analyzing metadata for evidentiary purposes in civil proceedings. Through a comprehensive review of current literature, legal cases, and forensic techniques, it explores the diverse approaches to metadata analysis across various digital domains, including file systems, emails, documents, web browsers, mobile devices, cloud storage, social media, and emerging technologies. The study highlights the critical role of metadata in establishing the authenticity, reliability, and chronology of digital evidence. It also addresses the challenges posed by encrypted data, large-scale analysis, and the need for robust quality assurance processes. The findings underscore the importance of adapting forensic methodologies to evolving digital landscapes while maintaining legal and ethical standards. This research contributes to the ongoing development of best practices in digital forensics and their application in civil litigation.

GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques—BERT's deep contextual understanding and the generative capabilities of GPT—to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.

EXTENDED SERIAL GRAPH-VALIDATION QUEUE SCHEME WITH LOCKING STRATEGY

In today's digital landscape, collaborative work in real-time is on the rise, allowing individuals to connect across different locations through applications facilitated by client-server architecture, enabling users to access and work on the same project simultaneously. However, clients' simultaneous access and modifications to the database can result in data inconsistencies, underscoring the importance of concurrency control. Managing concurrent transactions can introduce complexities and potentially adversely impact server performance. Object caching emerges as a viable solution as an alternative approach to handling transaction traffic. Extended Serial Graph-Validation Queue (Extended SG-VQ) is a control concurrency scheme that operates within the client-server architecture framework and incorporates object caching. The cache component implements a queue-based validation algorithm as part of its validation process. At the same time, the server-side employs a graph-based validation algorithm with locking strategies. Through a series of hypothetical transaction scenarios across three cases, this study validates the effectiveness of the Extended SG-VQ, demonstrating its ability to utilize serial graphs, resolve conflicts, and identify cyclic patterns.

Enterprise Integration as a Driving Factor for Guiding Digitalization in a Manufacturing Small and Medium Enterprise

In the face of rapid technological advancements and evolving consumer demands, businesses increasingly turn to digitalization to remain competitive and redefine industry standards. This article delves into the crucial role of enterprise integration within the context of digital transformation, with a specific focus on manufacturing Small and Medium Enterprises (SMEs). Based on fieldwork within the Innovation Factory North project in Denmark, the study presents a three-dimensional framework – architecture, capabilities, and governance – that is instrumental in guiding manufacturing SMEs through the complexities of digitalization. The architecture dimension explores the dichotomy between monolithic and fog (edge) architectures, highlighting their respective merits and challenges. The capabilities dimension introduces the concept of operational capability stacking, demonstrating how specific information systems, when integrated, yield new capabilities. A list of common capabilities in manufacturing enterprises is provided, offering inspiration for manufacturing SMEs to enhance their operational efficiency. The governance dimension injects a strategic dimension into the digitalization process, emphasizing the need for a balanced approach between technology-driven and process-driven optimization methodologies. The research underscores the strategic nature of digitalization in manufacturing SMEs, recognizing the interconnectedness of architecture, capabilities, and governance in achieving sustained success in the digital landscape. The proposed three-dimensional framework is substantiated through real-world applications within Denmark’s Innovation Factory North project, providing tangible insights and scenarios to navigate the intricacies of the digital landscape effectively.

DEVELOPING A MARKETING STRATEGY USING SWOT ANALYSIS IN THE DIGITAL ERA ON MOOI DONUT MSMES ON KALIMANTAN STREET, SUMBERSARI DISTRICT, JEMBER REGENCY

Study aims to analyse the strategies to enchance the sales of Donat Mooi, a micro, small, and medium enterprise (MSME), in the digital era where consumers are increasingly adopting online purchasing. The study proposes using SWOT analysis as a strategy to address competition and improve the business's competitiveness. The focus is on identifying effective marketing alternatives to overcome challenges faced by Donat Mooi. The qualitative-descriptive method use with direct interviews, observations also literature reviews. It shows from SWOT analysis and Internal-External Matrix indicate the Donat Mooi currently holds a "Hold and Maintain" position. This analysis identifies the potentials and challenges encountered by the enterprise in the digital market. The recommended strategy emerging from this research is an intensive-aggressive approach, enabling Donat Mooi to leverage its internal strengths while addressing threats and capitalizing on external opportunities more effectively. Thus, this study not only provides insights into the current conditions of Donat Mooi but also offers concrete guidelines for actions aimed at enhancing competitiveness and ensuring the sustainability of the business in an evolving digital landscape.

Sentiment Analysis of Oppenheimer Movie Reviews: Naïve Bayes Algorithm for Public Opinion

The development of information and communication technology has revolutionized the way people consume and engage with media, particularly in the realm of film. Online platforms such as Netflix, Amazon Prime Video, and YouTube have transformed movie consumption habits, providing a vast array of options for viewers to explore and enjoy. A crucial aspect of this digital landscape is the proliferation of movie reviews, which serve as valuable guides for users seeking to discover films aligned with their preferences. However, the abundance of reviews, often varying in quality and objectivity, necessitates tools capable of effectively processing and understanding these textual data. This research delves into sentiment classification of Oppenheimer movie reviews, utilizing the Naive Bayes algorithm to categorize reviews into positive, negative, and neutral sentiments. The dataset comprising audience reviews and numerical ratings undergoes preprocessing using the TF-IDF method to facilitate numerical representation. Subsequently, the Naïve Bayes algorithm is trained on this processed data to accurately classify sentiments. The model demonstrates exceptional performance, achieving an accuracy rate of 97.45% in distinguishing between positive, negative, and neutral sentiments within Oppenheimer movie reviews. This study underscores the efficacy of the Naive Bayes algorithm in sentiment classification and emphasizes the significance of employing techniques like TF-IDF for enhancing sentiment analysis in the domain of movie reviews.

Strategic approaches for successful digital transformation in project management across industries

Digital transformation has become imperative for organizations across industries seeking to enhance operational efficiency, innovate customer experiences, and maintain competitive advantage in a rapidly evolving digital landscape. This paper explores strategic approaches that enable successful digital transformation in project management, addressing key challenges and leveraging opportunities for growth and sustainability. In the current business environment, digital transformation in project management encompasses the adoption of advanced technologies, integration of digital tools, and reengineering of processes to optimize project delivery and outcomes. Organizations are increasingly leveraging cloud computing, artificial intelligence, big data analytics, and Internet of Things (IoT) to streamline project workflows, enhance decision-making capabilities, and facilitate real-time collaboration among stakeholders. Successful digital transformation initiatives in project management are underpinned by strategic planning and leadership commitment. Alignment of digital transformation goals with organizational objectives ensures clarity of purpose and enhances stakeholder engagement throughout the transformation journey. Moreover, fostering a culture of innovation and continuous learning enables teams to adapt to technological advancements and embrace change effectively. Challenges in achieving successful digital transformation include legacy system integration, data security concerns, skill gaps among team members, and resistance to cultural change. Addressing these challenges requires proactive risk management, investment in training and development programs, and collaboration with technology partners to navigate complexities and ensure seamless implementation. Case studies illustrate diverse approaches to digital transformation in project management, highlighting best practices and lessons learned across various industries. Organizations that successfully navigate digital transformation not only achieve operational efficiencies and cost savings but also create value through enhanced customer experiences and improved competitive positioning. Looking forward, the strategic adoption of emerging technologies and agile methodologies will continue to shape the future of digital transformation in project management. Organizations must remain adaptable and responsive to market dynamics, leveraging digital tools to drive innovation, accelerate time-to-market, and sustain long-term growth in an increasingly digital-first world. In conclusion, strategic approaches to digital transformation in project management empower organizations to harness the full potential of digital technologies, driving efficiencies, fostering innovation, and achieving sustainable success across industries.

The Transformative Impact of Financial Technology (FinTech) on Regulatory Compliance in the Banking Sector

Financial Technology (FinTech) has emerged as a disruptive force in the banking sector, revolutionizing the way financial services are delivered and consumed. This review explores the transformative impact of FinTech on regulatory compliance within the banking industry. The integration of advanced technologies such as artificial intelligence, blockchain, and big data analytics has enabled financial institutions to enhance operational efficiency, improve customer experience, and expand market reach. However, these innovations have also posed unprecedented challenges to traditional regulatory frameworks designed to safeguard financial stability and consumer protection. This review examines how FinTech innovations have necessitated regulatory adaptation and evolution. It highlights the complexities introduced by novel financial products, digital payment systems, and decentralized finance (DeFi) platforms, which often operate beyond conventional regulatory boundaries. Regulatory compliance in areas such as anti-money laundering (AML), know your customer (KYC) requirements, and data privacy has become more intricate as FinTech solutions blur geographical and jurisdictional lines. Moreover, the strategies employed by regulatory bodies and financial institutions to address these challenges effectively. These include leveraging regulatory technology (RegTech) solutions for enhanced monitoring and compliance automation, fostering collaboration between regulators and industry stakeholders, and advocating for agile regulatory frameworks capable of accommodating rapid technological advancements. Looking ahead, the review anticipates ongoing shifts in regulatory paradigms to accommodate the transformative impact of FinTech. It emphasizes the importance of proactive regulatory approaches that balance innovation with risk management, ensuring the integrity and resilience of the banking sector amidst a rapidly evolving digital landscape. This provides a comprehensive overview of how FinTech is reshaping regulatory compliance in banking. It underscores the need for adaptive regulatory strategies and collaborative efforts to harness the full potential of FinTech while safeguarding financial stability and consumer trust.

Digital propaganda is not simply propaganda in digital garb: toward an expanded theory of propaganda

Abstract Drawing on a critical review of the existing literature on computational propaganda and disinformation, and employing a three-stage process—addressing the “New Wine in Old Bottles” problem, extracting foundational concepts, and constructing a four-pillar framework—this article proposes an expanded theory of propaganda. The theory posits that digital propaganda is shaped by four key dimensions: politico-economic, sociocultural, technological, and socio-psychological, further delineated by the forces of commodification, privatization, connectivity, and virality. Broadening the analytical scope, it encompasses intricate interactions among politics, content, actors, platforms, and goals, recognizing the dynamic complexities inherent in the digital landscape. Furthermore, it sheds light on how commercial interests impact the production and dissemination of propaganda, offering insights into the propagation of popular ideologies such as patriotism and populism. This advances the understanding of digital propaganda’s pervasive impact on political discourse and societal attitudes, encouraging broader global research beyond a focus on state actors.

Assessing Power and Hierarchy in Cyberspace: An Approach of Power Transition Theory

This study explores the application of Power Transition Theory (PTT) to cyberspace, aiming to establish a comprehensive framework for understanding and measuring cyber power. Utilizing PTT’s national power model, the research treats states as rational and unitary actors, integrating the rational actor model to assess state behavior in cyberspace. The objectives include defining cyber power, developing a novel metric for its evaluation, and categorizing states within a hierarchical structure of cyber power. By analyzing key components such as data resources, digital economic strength, and cyber political capacity, the study provides a nuanced understanding of cyber power dynamics. The results demonstrate that the traditional IR theories retain relevance in the cyber domain, offering a valuable lens for comprehending global cyber governance and geopolitical competition. This foundational work sets the stage for future analyses of power transitions within cyberspace, highlighting the critical interplay between traditional power metrics and emerging digital landscapes.

Tarjamat al-Aghani al-Indunisiyah Ila al-‘Arabiyah Ala Dhou’i al-Shoutiyyat al-‘Arabiyah

With the widespread use of social media, particularly the YouTube platform, it has emerged as one of the most significant phenomena in the current digital landscape. It serves not only as a medium for sharing and accessing visual content but also as a space for various forms of creativity, innovation, invention, and information. Its extensive use is evident in the rapidly increasing amount of content covering diverse topics such as entertainment, product reviews, and education, including the delivery of learning materials, learning activities, instructional models, and even song translations from one language to another. The purpose of this study is to analyze errors in the translation of syllables (maqtha’) in Indonesian songs translated into Arabic on several YouTube channels. The approach used in this research is qualitative with a descriptive-analytical method. The main data sources are translated song lyrics from the channels “Kampung Arab Al-Azhar” and “Inspirasi Arabia”, both focusing on the same theme, namely “Mungkin”. Secondary sources include books, magazines, journals, and scholarly articles closely related to the topic. Data collection methods utilized include observation and documentation on one of the relevant YouTube channels. The research findings indicate that Arabic phonetics play a crucial role in translating Indonesian songs into Arabic to maintain the beauty of the target language by ensuring syllable harmony between the source and target languages.

Detecting adolescent depression in social media: A hierarchical ensemble learning approach

As digital landscapes evolve, adolescents increasingly rely on social media platforms for self-expression, leading to the vast dissemination of their mental and emotional states. Amidst the physiological and psychological transitions characteristic of adolescence, there is a heightened risk of depressive disorders. These shifts, coupled with the dynamic nature of their online expressions, create a compelling case for detecting latent signs of depression within their digital footprints. This thesis delves into the intersection of Natural Language Processing (NLP) and adolescent depression detection, introducing an innovative hierarchical ensemble model tailored for the intricate task of identifying depressive markers in adolescent social media content. This model amalgamates traditional word embeddings with state-of-the-art pretrained models, encapsulating both sentence-level and word-level representations. Empirical validation, conducted on a unique dataset centered on adolescent depression detection, indicates the models superior efficacy over existing baselines. By offering an ensemble approach that captures the nuances of adolescent linguistic expressions, this research illuminates the potential for timely and non-intrusive interventions in adolescent mental health.

Robust image tamper detection and recovery with self-embedding watermarking using SPIHT and LDPC

In today’s digital landscape, the pervasive use of digital images across diverse domains has led to growing concerns regarding their authenticity and reliability. The potential for malicious manipulation of these images underscores the critical need to develop robust methods for detecting tampering and ensuring their integrity. Fragile watermarking has been found to have extensive applications for tamper detection and recovery. An image watermarking technique for tamper detection, correction, and recovery is presented in this study. The proposed method employs a self-embedding method to generate the reference watermark from the original image, which has the advantage of superior tamper detection, localization, recovery capabilities, and robustness against attacks. Set Partitioning in the Hierarchical Tree (SPIHT) algorithm is applied to generate a reference watermark of the original image. Low-Density Parity Check (LDPC) is employed for error correction, providing higher-quality reconstruction to recover the original image. Schur decomposition processed the watermarked image blocks to generate authentication bits for each block to enhance tampering detection. The proposed watermarking method was evaluated using PSNR, SSIM, BER, and NC metrics for grayscale and colored images. The technique demonstrated high robustness in tamper detection and recovery against various malicious attacks. Comparative analysis with existing methods shows the efficacy of the proposed method.

Redefining Enterprise Data Management with AI-Powered Automation

In today's rapidly evolving digital landscape, the volume of enterprise data has surged exponentially, posing significant challenges in effective data management. Traditional data management techniques are becoming increasingly inadequate to handle the complexity and scale of modern enterprise data. This paper presents an innovative approach to revolutionize enterprise data management through AI-powered automation, a solution that enhances accuracy, efficiency, and decision-making processes within organizations. By leveraging advanced artificial intelligence technologies, such as machine learning, natural language processing, and predictive analytics, our proposed system aims to streamline data processing, ensure data quality, and provide real-time insights. This paper will discuss the limitations of existing data management systems, illustrate the novel methodologies integrated within our AI-driven framework, and demonstrate the system's efficacy through empirical results. The transformative potential of AI in automating data management processes not only addresses current challenges but also sets a foundation for future advancements in the field. As enterprises strive to maintain a competitive edge, the adoption of AI-powered automation for data management is not merely an option but a necessity for sustaining growth and innovation.