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This study aims to analyze the effect of artificial intelligence (AI)-based demand forecasting and big data analytics on inventory optimization through prediction accuracy among e-commerce actors or marketplace sellers in Curug. This research employed an explanatory quantitative approach involving 100 respondents selected through purposive sampling based on predefined criteria relevant to the research objectives. Data were collected using a structured Likert-scale questionnaire and analyzed using Structural Equation Modeling Partial Least Squares (SEM-PLS) by evaluating the measurement model, structural model, and mediation effects. The findings reveal that AI-based demand forecasting and big data analytics have a positive and significant effect on prediction accuracy. Furthermore, prediction accuracy has a positive and significant effect on inventory optimization and significantly mediates the relationship between AI-based demand forecasting, big data analytics, and inventory optimization. These findings indicate that the integration of AI and big data analytics contributes to more accurate demand prediction, leading to improved inventory management performance. The implication of this study suggests that e-commerce actors should improve data quality, analytical capability, and the adoption of predictive technologies to support more accurate, efficient, and responsive inventory decisions, thereby enhancing operational performance and competitiveness in responding to dynamic market demand.      

This study examines the transformation of business education curricula in response to the rapid expansion of the digital economy. The increasing integration of artificial intelligence, big data analytics, cloud computing, and digital platforms has significantly reshaped industrial demands and professional competencies. Using a qualitative research approach and content analysis, this study explores the challenges and strategic opportunities associated with curriculum transformation in higher education institutions. The findings reveal that universities face critical challenges related to technological infrastructure, faculty readiness, curriculum relevance, and industry alignment. However, digital transformation also provides opportunities for interdisciplinary learning, experiential education, global collaboration, and technology-driven innovation. The study emphasizes the importance of adaptive leadership, faculty development, industry partnerships, and student-centered pedagogies in supporting sustainable curriculum reform. Ultimately, transforming business education curricula is essential for preparing graduates with digital competencies, critical thinking abilities, and innovative skills necessary to compete effectively in the global digital economy.

Digital transformation in Human Resource Management (HRM) has shifted the organizational paradigm from an administrative function to a strategic function focused on adaptive and sustainable human resource development. This study aims to systematically review the integration of digital technology in HRM practices with particular emphasis on inclusivity and organizational sustainability. The method employed is a literature review analyzing various studies related to the implementation of Artificial Intelligence (AI), big data analytics, and digital systems in recruitment, competency development, and employee performance evaluation. The findings indicate that digital transformation enhances operational efficiency, decision-making quality, and workplace flexibility. However, the adoption of digital technology also creates ethical challenges, including algorithmic bias, unequal access to technology, and concerns regarding employee data privacy. Therefore, the implementation of Equity, Diversity, and Inclusion (EDI) principles is essential in developing fair and inclusive HRM systems. Furthermore, continuous learning cultures and flexible work models have proven effective in supporting employee well-being while strengthening organizational resilience in facing global changes. This study emphasizes the importance of synergy between technological innovation, ethical leadership, and sustainability in building HRM systems that are responsive to the future of work.

.This study aims to analyse the role of Financial Information Systems (FIS) in supporting risk management, decision-making, and organisational performance in the digital transformation era. This study employs the Systematic Literature Review (SLR) method to examine articles indexed in Scopus from 2016 to 2026. The PRISMA framework is used to ensure a systematic, transparent article selection process, resulting in the selection of 37 relevant articles for further analysis. The results of the study show that Financial Information Systems make a major contribution to improving financial transparency, operational efficiency, the quality of strategic decision-making, and organisational risk mitigation. In addition, the integration of emerging technologies such as Artificial Intelligence (AI), FinTech, big data analytics, and cloud computing further strengthens the effectiveness of financial information systems in modern organisations. This study contributes theoretically by mapping research trends and identifying research gaps, while providing practical benefits for organisations seeking to increase competitiveness through digital financial systems. For future research, it is recommended to develop a more predictive and intelligent Financial Information Systems model to address future business dynamics.

Tax administrations are undergoing a fundamental transition from conventional audit practices based on manual inspection and limited sampling toward data-driven supervision supported by big data analytics, artificial intelligence, and digital transaction infrastructures. However, developing economies, particularly in Southeast Asia, continue to face structural constraints such as fragmented legacy systems, informal economic activities, uneven digital literacy, corruption risks, weak data interoperability, and evolving privacy regulations. This study aims to develop a contextual framework for detecting potential tax-reporting fraud by integrating big data tax analytics, localized machine learning, explainable artificial intelligence, blockchain-enabled value-added tax data integrity, and socio-organizational governance. The study adopts a mixed-method sequential explanatory approach combined with Design Science Research. The methodological design integrates policy and institutional analysis, machine learning model design, and socio-organizational validation using secondary literature, Southeast Asian case studies, regulatory review, and simulated data architecture. The main contribution of this study is the Contextual Tax Analytics with AI and Blockchain Framework, or C-TAX-AIB Framework, consisting of three interrelated layers: Data Layer, Analytics Layer, and Governance and Human Layer. The Data Layer proposes a hybrid blockchain architecture for e-Faktur and value-added tax reporting integrity; the Analytics Layer introduces localized machine learning and explainable AI to support transparent risk scoring and anomaly detection; and the Governance and Human Layer embeds privacy protection, taxpayer digital literacy, auditor readiness, and trust-building mechanisms. The framework advances prior studies by moving beyond algorithmic fraud detection toward an integrated governance model suitable for developing economies. The study provides theoretical implications for public finance analytics and practical guidance for ASEAN tax administrations in designing accountable, explainable, and context-sensitive digital tax systems.

This study explores the role of Business Intelligence (BI) in improving operational effectiveness and company performance amid rapid digital transformation, big data, Artificial Intelligence (AI), and data analytics development. Using a Narrative Literature Review (NLR) approach, this study analyzed 48 national and international scientific articles related to BI, firm performance, digital transformation, and big data analytics. The findings reveal that BI positively influences decision-making quality, operational efficiency, organizational agility, innovation capability, marketing effectiveness, customer relationship management, and competitive advantage. Furthermore, integrating BI with Big Data Analytics, predictive analytics, machine learning, and AI enhances organizational responsiveness to dynamic business environments. The study also identifies key success factors for BI implementation, including organizational readiness, management support, technological infrastructure, information quality, and system integration. These findings indicate that BI has evolved from a technological tool into a strategic organizational capability that supports sustainable business performance and digital transformation. This study contributes to the Business Intelligence literature by providing a comprehensive understanding of BI’s strategic role across various industries and organizational contexts.

This study aims to analyze a digital transparency strategy based on big data analytics in detecting village fund corruption in Indonesia. The increasing number of corruption cases indicates weaknesses in conventional monitoring systems that fail to integrate data comprehensively. This research employs a qualitative approach using literature review and document analysis of official reports from the Corruption Eradication Commission and Indonesia Corruption Watch. The findings reveal that big data analytics enables the detection of transactional anomalies, cross-sectoral data integration, and improved accountability in village fund management. Furthermore, this strategy enhances technology-based transparency through government digital systems. Therefore, the integration of digital transparency and big data analytics offers an innovative model for more effective and efficient prevention and early detection of village fund corruption. thus, the integration of digital transparency and big data analytics has the potential to become an innovative model for the prevention and early detection of village fund corruption in a more effective and efficient manner, while also supporting the strengthening of a data-integrated early warning system and adaptive public policy.

This study aims to provide a comprehensive analysis of Predictive Policing and its implications for law enforcement transformation in Indonesia, based on an extensive review of its global applications, benefits, and challenges. The study uses qualitative literature and international case study review methods to assess the impact and complexity of implementing digital technologies such as artificial intelligence (AI), machine learning, and big data analytics within a Predictive Policing framework. The results of this review highlight that while Predictive Policing offers significant potential for proactive crime prevention and increased operational efficiency, its implementation is consistently fraught with critical legal, ethical, and technical challenges, including regulatory gaps, risks of algorithmic bias, and data privacy concerns, which are particularly relevant to Indonesia. The findings underscore that public trust and police legitimacy in the context of adopting such technologies are strongly influenced by transparency, strong accountability mechanisms, and community involvement in shaping their use. This study contributes to the growing discourse on digital policing in developing countries and culminates in practical policy recommendations designed to guide the Indonesian police towards the development and implementation of Predictive Policing models that are effective, efficient, and fundamentally respectful of legal and human rights principles.

This study explores the role of visual analytics in enhancing decision-making processes within creative industries, focusing on its application to large-scale multimedia datasets. Visual analytics integrates interactive visualization techniques with computational algorithms, enabling users to explore complex datasets intuitively and derive actionable insights. The research centers on the design and implementation of interactive dashboards tailored to the creative sector, particularly film, music, and advertising industries, to facilitate real-time data exploration. The study also investigates the usability of these tools through expert-based evaluations, aiming to assess their effectiveness in supporting informed and timely decision-making. The findings reveal that interactive visualizations significantly improve insight discovery and pattern recognition, enabling decision-makers to uncover hidden trends in large multimedia datasets. However, challenges related to scalability, user acceptance, and real-time processing were encountered during the implementation phase. The research highlights the practical benefits of integrating visual analytics into industry workflows, which include enhanced content creation, audience engagement, and strategic planning. Furthermore, the study identifies key visual analytics techniques such as dynamic dashboards, pattern recognition, data mining, and clustering, which are essential for analyzing multimedia data. The study concludes by emphasizing the potential for wider applications of visual analytics in other sectors, suggesting future research directions to improve tool performance, scalability, and user accessibility, as well as exploring the integration of emerging technologies like artificial intelligence and virtual reality.

This study explores the transformative role of big data-driven Decision Support Systems (DSS) in global digital enterprises, particularly focusing on their impact on operational efficiency and corporate governance. By leveraging big data analytics, DSS offer organizations the tools to process vast amounts of real-time data, enabling executives to make more informed decisions that optimize resources, improve productivity, and reduce operational costs. The research highlights the integration of predictive analytics, machine learning, and real-time data processing within DSS, which allows businesses to gain strategic insights and anticipate market trends. Furthermore, the study emphasizes the significant role of DSS in enhancing corporate governance, improving transparency, accountability, and compliance with regulations. These systems foster better decision-making processes, which contribute to building trust among stakeholders and ensuring long-term organizational success. However, the study also identifies several challenges in implementing big data-driven DSS, including data management complexities, technological integration difficulties, and the need for skilled personnel. Despite these challenges, the findings demonstrate that big data-driven DSS are pivotal in driving competitive advantage, operational optimization, and governance improvements. The research concludes with actionable recommendations for executives to adopt and implement big data-driven DSS, emphasizing the importance of continuous support, training, and system integration. The study also suggests future research directions, including exploring the integration of emerging technologies like AI and IoT into DSS and assessing their long-term impact on sustainability and corporate governance.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

Advances in digital technology have led to significant transformations in management accounting practices, particularly with the use of cloud accounting, big data analytics, artificial intelligence (AI), and digital-based management accounting information systems. These changes have resulted in a shift in the function of management accounting from merely a documentation tool to a strategic decision support system that provides information quickly, accurately, and in real time. This study aims to analyze the implementation of management accounting in the digital business era, identify the obstacles faced by organizations in the digitization process, and explain the opportunities that can be utilized to improve the efficiency of financial management systems. The research method applies a qualitative approach by conducting a literature study that reviews a number of journals, books, and scientific documents related to the topic. The research findings indicate that digitization has a positive impact on operational efficiency, clarity of information, and the quality of managerial decision-making. However, organizations still encounter various challenges, such as low human resource technological capabilities, complexity in system integration, and increased threats to data security. This study concludes that the implementation of digital management accounting is a strategic necessity for companies in the modern business era, requiring technological readiness, increased human resource capacity, and internal policies that support a complete digital transformation process.  

The integration of big data in the financial sector has increasingly attracted scholarly attention, particularly in areas such as risk management, fraud detection, algorithmic trading, and investment optimization. Given the rapid development of this field, it is essential to map research trends and identify emerging directions that shape the future of financial innovation. This study applies a bibliometric approach using 3,829 articles retrieved from the Scopus database from 1981 to 2025, with data processed through R Studio and the Bibliometrix-Biblioshiny application. The objective is to explore the intellectual landscape of big data finance and reveal research frontiers as well as thematic evolution. The results show a sharp increase in publications after 2015, alongside the growth of fintech and artificial intelligence applications, with dominant themes including blockchain integration, risk analytics, and predictive modelling. Cross-disciplinary and cross-regional collaborations continue to expand. These findings provide a comprehensive overview of how big data has shaped financial studies and offer insights for potential future research directions.

Digital transformation in the supply chain is the main driver of increasing industrial competitiveness in the modern era. This study aims to analyze the influence of supply chain digitalization on industrial production efficiency through information integration, process optimization, and strengthening collaboration between supply chain actors. The method used is a qualitative approach based on literature studies on various previous studies that discuss the application of digital technologies, such as the Internet of Things (IoT), big data analytics, blockchain, and Enterprise Resource Planning (ERP), in industrial supply chain management. The results of the study show that digitalization increases the visibility of supply chain activities in real-time, allowing companies to monitor the movement of raw materials, production processes, and distribution more accurately. Digital integration also strengthens the company's internal and external coordination, thereby accelerating decision-making and increasing the effectiveness of collaboration with supply chain partners. In addition, the application of digital technology encourages the optimization of production processes through automation, more precise planning of material needs, and reduction of uncertainty of demand and supply. A number of studies report a decrease in lead-time, operational cost efficiency, and improved product quality as a positive impact of digitalization. However, the literature also identifies implementation challenges, such as technology gaps, resistance to organizational change, and significant initial investment needs. Overall, this study concludes that supply chain digitalization is a crucial strategy to improve production efficiency, operational resilience, and company responsiveness to market dynamics, as well as provide a basis for the development of industrial digitalization strategies and advanced research.

Digital transformation has significantly reshaped human resource management (HRM) through the adoption of Human Resource Information Systems (HRIS), artificial intelligence (AI), big data analytics, e-learning platforms, and remote work technologies. Although these innovations improve efficiency and decision-making, they also generate ethical challenges related to data privacy, algorithmic bias, transparency, and employee monitoring. This article examines the role of professional ethics in HRM within the context of digital transformation, highlighting both emerging challenges and potential opportunities. This study employs a conceptual research approach supported by a comprehensive literature review of scholarly works on HRM, professional ethics, and digitalization. The analysis focuses on core ethical principles such as integrity, fairness, responsibility, professionalism, and confidentiality, and evaluates their implementation in digital HR practices. The findings indicate that unethical use of digital technologies may lead to discrimination, reduced employee trust, and violations of individual rights, particularly through biased AI-based recruitment systems and opaque performance evaluation mechanisms. However, digital transformation also offers opportunities to strengthen ethical HR governance. The use of ethical data management, algorithmic audits, digital transparency, and e-learning-based ethics training can enhance accountability and fairness in HR processes. The study concludes that integrating professional ethics with digital HRM is essential for developing human-centered, sustainable, and trustworthy organizations in the digital era.

The development of digital technology, big data, and artificial intelligence has fundamentally changed the way scientific knowledge is produced, validated, and used. The classical scientific paradigm, which relies on objectivity, linearity of methods, and empirical verification, is no longer entirely adequate to explain contemporary phenomena that are complex, dynamic, and non-linear. In this context, this study highlights the urgency of reconceptualizing science and scientific methods in order to remain relevant to modern research challenges. This article aims to identify the limitations of the classical scientific paradigm in understanding modern reality, analyze the transformation of scientific methods as a result of developments in computational technology and data analytics, and formulate directions for reconceptualizing science and research methodologies in line with the needs of the digital age. The research uses a conceptual approach through the integration of theory, epistemological criticism, and empirical findings from previous studies The results of the study show that classical epistemology is insufficient to capture the adaptive and networked phenomena that arise from socio-technological interactions. Scientific methods are shifting towards non-linear, data-driven, and complexity-based approaches, where patterns can be identified before hypotheses are formulated. In addition, technologies such as machine learning and predictive computing are forming new epistemic structures that impact scientific validity. The implications of the study emphasize the need for multi-method mastery, digital and algorithmic literacy, and the integration of ethical principles as core components of modern scientific methodology.  

The development of digital technology has significantly changed the way organizations manage human resources (HR), both in the public and business sectors. Organizations are now required to transform from an administrative HR management system to a technology-based strategic role to improve efficiency, competitiveness, and economic productivity. The results show that HR management transformation plays a significant role in improving organizational performance through the implementation of digital technologies such as Human Resource Information Systems (HRIS), people analytics, big data, and artificial intelligence (AI). This transformation can improve work efficiency, accelerate decision-making processes, and encourage innovation and collaboration. In the public sector, human resource digitalization supports bureaucratic reform towards transparent and accountable governance, while in the business sector, it strengthens employee competitiveness and creativity. However, key challenges identified include the digital competency gap, resistance to change, and limited technological infrastructure, particularly in public organizations and developing regions. The study concluded that the success of human resource transformation depends not only on technology adoption but also on organizational cultural readiness, visionary leadership, and the continuous development of digital competencies. Synergy between people and technology is key to achieving sustainable economic productivity in the digital era.

Innovation in healthcare organization management in the digital era plays a crucial role in enhancing the efficiency, accessibility, and quality of healthcare services. As digital technologies continue to evolve, healthcare institutions are increasingly adopting systems such as Electronic Medical Records (EMR), Hospital Management Information Systems (HMIS or SIMRS), telemedicine platforms, mobile health applications, Big Data analytics, and Artificial Intelligence (AI) to streamline operations and improve patient outcomes. This literature review highlights how the implementation of these technologies can accelerate service delivery, reduce administrative burdens, support data-driven clinical and managerial decision-making, and enable remote healthcare access—particularly beneficial in rural or underserved areas. For instance, EMRs improve the accuracy and availability of patient data, while AI can assist in diagnostics and predictive analytics for disease prevention. However, despite these promising developments, several significant challenges persist. These include limited digital infrastructure in some regions, low levels of digital literacy among healthcare professionals, high implementation and maintenance costs, organizational resistance to technological change, and concerns over data privacy and cybersecurity. Therefore, successful digital transformation in healthcare is not solely dependent on technology itself. It also requires strong organizational readiness, supportive regulatory frameworks, investment in workforce training and development, and effective change management strategies. Collaborative efforts between government, private sector, and healthcare providers are essential to overcome these barriers. In conclusion, innovation in healthcare management must be holistic—integrating technological advancements with human, organizational, and policy-level adaptations—to ensure the delivery of high-quality, efficient, and sustainable healthcare services in the digital age.

To respond to the challenges and needs of contemporary society, the public sector must rapidly adapt to digital transformation. The objective of this research is to examine relevant and adaptive human resource development strategies for the digital ecosystem and to evaluate how they impact the quality of public services in the era of technological disruption. This research uses a descriptive qualitative approach with a literature review and policy analysis. It analyzes best practices from government institutions, both national and international, in developing human resources oriented towards the digital era. Key findings indicate that optimizing human resources requires not only improving technological capabilities or digital expertise; it also requires reconstructing leadership paradigms, flexible organizational cultures, and implementing meritocratic systems and data-driven performance management. It is evident that technologies such as big data analytics, artificial intelligence (AI), and the Internet of Things (IoT) can help improve public services, but the success of these technologies depends heavily on the capabilities and readiness of the employees who manage these systems. An integrated digital talent ecosystem must be built, encompassing continuous training (learning for life), collaboration between government, academia, and business (the triple helix model), and a regulatory framework responsive to technological developments. Furthermore, it is emphasized that developing digital integrity and ethics is crucial as a pillar of good governance in the digital era. Optimizing human resource development strategies systematically and sustainably will enable Indonesia to improve the efficiency of public services and strengthen the competitiveness of its bureaucracy globally. By 2045, adaptable, innovative, and highly integrated human resources will be the primary drivers of a digital government transformation that is inclusive, responsive, and future-oriented.