Abstract:This study aims to examine the role of competition law in regulating corporate conduct, protecting consumers, and enhancing economic efficiency in contemporary market economies. Amid increasing market concentration, digital…
tal platform dominance, and the emergence of data-driven business models, competition law has become an essential regulatory instrument for ensuring fair competition, safeguarding consumer interests, and promoting sustainable economic development. This study employs a Systematic Literature Review (SLR) based on the PRISMA 2020 framework. Relevant literature was systematically collected from six major academic databases, namely Scopus, Web of Science, ScienceDirect, SpringerLink, Emerald Insight, and Taylor & Francis Online. The review process included identification, screening, eligibility assessment, and inclusion stages. A total of 78 peer-reviewed articles published between 2015 and 2025 were selected and analyzed using thematic synthesis techniques. The findings reveal that competition law performs four interconnected functions. First, it serves as a regulatory mechanism that shapes corporate behavior and prevents anticompetitive practices, including monopolization, cartel agreements, price-fixing, and abuse of dominant positions. Second, competition law enhances consumer welfare by promoting competitive prices, product quality, innovation, and consumer choice. Third, effective competition policy contributes to allocative, productive, and dynamic efficiency, thereby supporting long-term economic growth. Fourth, digital markets introduce new challenges associated with data concentration, platform dominance, network effects, and algorithmic pricing, requiring adaptive regulatory frameworks and strengthened institutional capacity.This study contributes to the literature by integrating Economic Efficiency Theory, Consumer Welfare Theory, Competition Policy Theory, and Regulatory Governance Theory into a comprehensive analytical framework that explains the relationship between competition law, corporate conduct regulation, consumer protection, and economic efficiency.The findings provide policy recommendations for competition authorities and governments, particularly in developing economies, regarding digital competition governance, institutional strengthening, cross-border enforcement cooperation, and data-driven market regulation.Unlike previous studies that focus on isolated dimensions of competition law, this research offers a holistic synthesis of legal, economic, consumer welfare, and governance perspectives. It further highlights how competition law can address emerging challenges in the digital economy while simultaneously promoting consumer protection and economic efficiency.
Abstract:The purpose of this study is to investigate the relationship beetween job satisfaction and work stress on turnover intention as well as organizational commitment mediates this reliationship. To gather study data, the questionnaire…
stionnaire was used to poll 100 individuals. Structural Equation Modeling (SEM) was the method used in this quantitive study. SmartPLS version 4.1.1.6 with the PLS-SEM Algorithm and Bootstrapping was used to analyze the data. The findings demonstrated that while work stress had a positive but negligible impact on turnover intention, job satisfaction and organizational commitment had a negative and significant influence. Furthermore, the findings indicate that organizational commitment is significantly impacted negatively by work stress and positively by job satisfaction. Additionally, whereas organizational commitment can moderate the association between work stress and turnover intention, it cannot mediate the relationship between job satisfaction and turnover intention. This study has a results to provide important managerial implications for managing employees' turnover intention tendencies
Abstract:The rapid proliferation of AI-powered marketing technologies in emerging markets poses a fundamental challenge to culturally-grounded micro, small, and medium enterprises (MSMEs): how can algorithmic imperatives be reconciled…
ciled with indigenous value systems that define not only business practice but collective identity? Despite growing research on both AI adoption in SMEs and indigenous knowledge preservation, scholarship rarely examines how traditional values actively mediate rather than merely moderate commercial technology adoption. This study addresses that gap by investigating how MSMEs in Makassar City, Indonesia, negotiate AI marketing integration while preserving siri’ na pacce, the Bugis-Makassar philosophical framework centred on dignity (siri’) and solidarity (pacce). Employing interpretive phenomenology integrated with Community-Based Participatory Research (CBPR), the study conducted 23 in-depth interviews and three focus group discussions with 44 MSME owners and key personnel across traditional culinary, artisan craft, ethnic fashion, and digital service sectors. Template analysis generated four overarching themes: (1) value-based technology discernment, wherein siri’ na pacce operates as an active epistemological filter for evaluating AI tools; (2) strategic selective adoption, wherein enterprises accept algorithmically aligned functions while rejecting culturally incompatible features; (3) cultural indigenization of technology, wherein AI systems are actively reoriented toward communal rather than individualistic ends; and (4) constrained agency under platform power, wherein algorithmic visibility systems penalise cultural non-conformity with market exclusion. These findings challenge technological determinism and advance decolonial computing theory by demonstrating that indigenous values simultaneously enable epistemological agency and are constrained by structural power asymmetries, a duality insufficiently theorised in prior technology adoption frameworks. The study calls for regulatory frameworks establishing indigenous data sovereignty, participatory AI co-design with local communities, and cooperative digital infrastructure as conditions for authentic, rather than performative, cultural integration.
Abstract:In an uncertain economic climate, a large global retailer used AI-powered spend intelligence to move $220 million from indirect operational costs toward high-impact R&D. In a difficult recession, this decisive step boosted…
ed revenue by 11%, demonstrating the transformative impact of effective capital management. This achievement contrasts with "spend blindness," where industry studies show most financial leaders struggle to link expenditure patterns to strategic growth outcomes and resort to reactive cost-cutting. This study addresses this crucial gap. A thorough mixed-methods approach including a global survey of 400 CFOs, longitudinal case studies of ten multinational organizations, and advanced predictive modeling substantiated a new paradigm. Research shows that companies that understand AI-driven spend orchestration develop 7.3% faster than competitors. This premium comes from a 37% improvement in the Growth Efficiency Ratio (GER), a critical statistic for translating savings into innovation, and 5.8 times more strategic investment opportunities than standard financial approaches allow. The Spend Intelligence Quotient (SIQ), a groundbreaking statistic that assesses financial agility through integrated spend monitoring, predictive analytics, and rapid capital reallocation, is key to this advantage. This paper introduces the empirically based Spend Orchestration Framework and the requirements for the 2025 AI Finance Stack to obtain SIQ >80, the empirically proven threshold for sustainable competitive advantage. The message is clear: finance chiefs must go beyond oversight. Today's CFO may use predictive contracting and algorithmic governance to turn spend data into strategic leverage, ensure resilience, and capture disproportionate value in.
Abstract:The era of artificial intelligence (AI) has brought significant changes in Strategic Human Resource Management (SHRM). This study aims to explore organizational adaptation strategies in facing the integration of AI in SHRM…
RM after 2024. Using the Scoping Review method, this study identifies key trends, challenges, and best strategies in implementing AI in HR management. The review results show that AI improves efficiency in recruitment, performance evaluation, and employee skills development, but also presents ethical challenges such as algorithmic bias and personal data protection. In addition, companies that are successful in adopting AI implement reskilling and upskilling strategies to ensure workforce readiness. This study provides insights for academics and practitioners in developing HR policies that balance technological efficiency and a human value-based approach.
Abstract:Artificial Intelligence has become a transformative technology in the field of financial technology, leveraging advanced algorithms and machine learning to identify risks and make informed decisions. However, its widespread…
ead adoption presents new challenges related to ethical use, data privacy, security concerns, potential bias, and discrimination. This study aims to explore the benefits of AI-based risk management in Fintech while highlighting associated challenges and providing recommendations. This research utilises the systematic review methodology to analyse existing literature and identify important patterns, gaps, and areas for further investigation. The study utilised data gathered from the Scopus database to obtain credible scholarly materials. Research data was collected from a variety of countries including the United States, China, European nations, and other Asian countries in order to develop a comprehensive understanding of AI-based risk management on a global scale. The findings highlight the crucial role of ethical considerations in implementing AI-based risk management systems to ensure fairness, transparency, and accountability. Moreover, the fintech industry needs to establish strong data protection measures and address issues related to bias and discrimination in order to instil trust and uphold public confidence in AI-based risk management. Future research should emphasise assessing the effectiveness of different algorithms and approaches while also examining potential regulatory frameworks and legal implications associated with AI-based risk management strategies.
Abstract:Masalah pengelolaan parkir di Politeknik Jambi sering menyebabkan ketidakefisienan akibat sistem penghitungan manual yang rentan human error. Penelitian ini bertujuan merancang sistem penghitungan kendaraan otomatis berbasis…
asis pengolahan citra digital. Metode yang digunakan adalah Background Subtraction dengan algoritma MOG2 dan OpenCV menggunakan video kamera smartphone. Sistem mengintegrasikan proses preprocessing citra, operasi morfologi, dan Euclidean Distance Tracker untuk melacak serta menghitung kendaraan secara real-time. Hasil penelitian menunjukkan sistem mampu membedakan kendaraan yang bergerak dengan objek statis secara akurat melalui Virtual Counting Line. Dengan beban komputasi yang ringan dan biaya rendah, sistem ini efektif menjadi solusi otomatisasi manajemen parkir di lingkungan kampus.
Parking management issues at Politeknik Jambi often lead to inefficiencies due to manual counting systems prone to human error. This study aims to design an automatic vehicle counting system based on digital image processing. The method utilizes Background Subtraction with the MOG2 algorithm and OpenCV using smartphone video input. The system integrates image preprocessing, morphological operations, and Euclidean Distance Tracker to track and count vehicles in real-time. The results demonstrate that the system can accurately distinguish between moving vehicles and static objects via a Virtual Counting Line. With low computational requirements and cost-effectiveness, this system offers an efficient automation solution for campus parking management.
Abstract:Emas merupakan salah satu jenis komoditi yang paling banyak diminati untuk tujuan investasi, karena dipandang sebagai instrumen yang lebih aman dibandingkan saham serta memiliki nilai jual yang selalu bergerak mengikuti…
kondisi pasar. PT Victoeria Vici, sebagai pelaku usaha perhiasan emas custom, menghadapi kendala dalam menentukan estimasi harga jual kepada pelanggan, sebab proses pengerjaan pesanan custom membutuhkan waktu hingga 14 hari, sementara harga emas bergerak fluktuatif dan tidak terstruktur setiap harinya sehingga estimasi harga menjadi tidak akurat dan tidak efektif. Berdasarkan permasalahan tersebut, penelitian ini menerapkan konsep Data Mining dengan algoritma Trend Moment untuk mengestimasi harga emas pada rentang waktu tertentu. Data yang digunakan merupakan data historis harga emas per gram pada PT Victoeria Vici periode Agustus–Oktober 2021 sebanyak 92 data. Tahapan penelitian meliputi pengumpulan data, penentuan variabel X dan Y, eliminasi untuk memperoleh nilai konstanta a dan slope b, serta penerapan persamaan Y = a + bX untuk memperoleh nilai estimasi. Hasil perhitungan menunjukkan nilai a = 720.871,725 dan b = 3,108 sehingga model estimasi mampu menghasilkan proyeksi harga emas yang mendekati pola data historis. Model ini kemudian diimplementasikan ke dalam aplikasi berbasis desktop menggunakan Microsoft Visual Basic 2010 dan basis data Microsoft Access, dilengkapi Crystal Report untuk pencetakan laporan hasil estimasi. Hasil penelitian menunjukkan bahwa algoritma Trend Moment dapat membantu PT Victoeria Vici dalam memperoleh estimasi harga emas secara lebih cepat, konsisten, dan terdokumentasi.
Gold is one of the most sought-after commodities for investment purposes, as it is regarded as a safer instrument compared to stocks and has a selling value that constantly fluctuates with market conditions. PT Victoeria Vici, a custom gold jewelry business, faces difficulty in determining the estimated selling price offered to customers because the production process for custom orders takes up to 14 days, while gold prices move in an unstructured and fluctuating manner every day, making manual price estimation inaccurate and ineffective. Based on this problem, this study applies the concept of Data Mining using the Trend Moment algorithm to estimate gold prices over a certain period of time. The data used is historical daily gold price data per gram from PT Victoeria Vici for the period of August–October 2021, consisting of 92 records. The research stages include data collection, determination of the X and Y variables, elimination to obtain the constant value a and the slope b, and the application of the equation Y = a + bX to obtain the estimated value. The calculation results show a value of a = 720,871.725 and b = 3.108, so that the estimation model is able to produce gold price projections that closely follow the pattern of historical data. This model was then implemented into a desktop-based application using Microsoft Visual Basic 2010 and a Microsoft Access database, equipped with Crystal Report for printing estimation result reports. The results show that the Trend Moment algorithm can help PT Victoeria Vici obtain gold price estimations more quickly, consistently, and in a well-documented manner.
Abstract:The development of artificial intelligence (AI) technology, especially deep learning, has made significant contributions in various fields, including the world of education. In the context of Islamic Religious Education…
(PAI), the biggest challenge today is how to deliver teaching materials effectively, adaptively, and relevant to the development of the times and the characteristics of the digital generation. Deep learning implementations offer a variety of opportunities to support a more personalized, interactive, and efficient learning process. Through an algorithm-based approach, deep learning is able to process student learning data to provide a learning experience tailored to individual learning styles. The application of this technology also allows for automatic assessment of students' understanding through text, voice, and expression analysis, so that teachers can focus more on providing character development and spiritual values. On the other hand, the use of AI-based chatbots and intelligent assistants can facilitate discussions and consultations about Islamic teachings instantly and factually. The methodology used in this study is a descriptive qualitative study with a literature study and case study approach. Data were collected through a review of national and international scientific literature and focused observations on the practice of AI implementation in several madrassas and Islamic schools in Indonesia. The analysis was carried out thematically by identifying patterns, opportunities, and challenges of applying deep learning in PAI learning. The results of this study are expected to make a scientific contribution to the development of technology-based Islamic education in the digital era.
Abstract:PayLater services are one of the rapidly growing digital financial innovations widely utilised in fintech apps in Indonesia, including Kredivo and Akulaku. User reviews on the Google Play Store reflect a range of experiences,…
nces, from satisfaction with the ease of use of the service to complaints regarding bills, interest rates, late payment fees, credit limits, and app performance. This study aims to classify the sentiment of user reviews regarding PayLater services on the Kredivo and Akulaku apps using the Multinomial Naïve Bayes algorithm. Data was collected via web scraping from the Google Play Store and automatically labelled based on user ratings, with ratings of 1-2 classified as negative sentiment and ratings of 4-5 as positive sentiment, whilst a rating of 3 was excluded as it was considered ambiguous. Following a preprocessing stage comprising cleaning, case folding, tokenisation, stopword removal, and stemming, as well as feature extraction using TF-IDF, 3,652 reviews were obtained with a training-to-test data split ratio of 80:20. The results indicate that positive sentiment dominates the dataset at 56.49%, whilst negative sentiment accounts for 43.51%. Analysis by application revealed that Kredivo was dominated by positive sentiment (68.20%), whilst Akulaku was dominated by negative sentiment (51.70%). The Naïve Bayes multinomial model achieved an accuracy of 84.13%, with average precision, recall, and F1-score values of 0.84, demonstrating good and balanced classification performance across both sentiment classes.