cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Danang
Contact Email
garuda@apji.org
Phone
+628995992828
Journal Mail Official
hanu@stekom.ac.id
Editorial Address
Jl. Majapahit No.304, Pedurungan Kidul, Kec. Pedurungan, Semarang, Provinsi Jawa Tengah, 52361
Location
Kota semarang,
Jawa tengah
INDONESIA
Journal of Management and Informatics
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 29617731     EISSN : 29617472     DOI : 10.51903
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
management and business economics involving operational management, management of human resources, finance management, marketing management, social and economic management
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Sistem Informasi Menejemen
Articles 83 Documents
 5   6   7   8   9 
Exploring the Role of Digital Tools in Ethical Managerial Decision-Making Álvarez, Miguel; Hassan, Leila
Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i3.306

Abstract

The rapid integration of digital technologies into managerial work reorganized ethical decision-making in organizations. Technology holds the promise of efficiency and transparency but leaves the impact of technology on moral thought and managerial accountability unclear. This research strives to analyze how digital technologies inform, enable, or complicate ethical managerial decision-making. Utilizing a qualitative exploratory research methodology, the research weaves evidence from an in-depth literature review and semi-structured interviews on Ethical Decision-Making Theory and Socio-Technical Systems Theory. Thematic coding suggests that online resources can enhance ethical consistency and moral awareness if used reflectively but reduce moral sensitivity based on reliance on algorithmic rationality. The findings suggest dynamic interplay between human judgment and computer mediation and underscore the merits of socio-technical integration under balance. A conceptual model for co-evolution modeling of digital intelligence and moral cognition in managerial contexts is proposed. Ethical decision-making in the digital era, this study argues, is less a question of algorithmic transparency and more a question of applying responsible human judgment in technologically mediated environments. The research makes a theoretical contribution by merging ethical thinking with socio-technical models of management and provides organisational practical advice on how to integrate moral thinking within digital environments of decision-making.
Implementation of Expert System Applications using Forward Chaining to Detect Dental Health Zahra , Zahra; Melyani, Melyani; Yusuf, Faif; Herawati, Metty Titin; Royanti, Suci; Rosihana, Athiy Dina
Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i3.212

Abstract

Dental diseases remain one of the most common health issues globally, often resulting from a lack of early detection and limited access to dental specialists. This research presents the implementation of an expert system that uses forward chaining to diagnose dental health conditions based on user-reported symptoms. The system integrates a knowledge base modeled from expert consultation with dentists, consisting of symptom sets and rule-based logic. Findings indicate that the Forward Chaining approach is effective for step-by-step rule evaluation and generates accurate diagnoses of diseases such as caries, gingivitis, periodontitis, halitosis, and pulpitis. The study demonstrates that expert systems can support preliminary dental screening and improve public awareness of dental health.
Value-Based Administration Services and Value-Based Care: Aligning Administrative Efficiency with Patient Outcomes Willie, Michael Mncedisi
Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i3.308

Abstract

Value-Based Care (VBC) is reshaping healthcare delivery by incentivising improved patient outcomes over service volume. However, its success is closely tied to the efficiency and responsiveness of administrative systems.This study introduces the concept of Value-Based Administration Services (VBAS) and explores how its integration with VBC can strengthen clinical performance, enhance operational efficiency, and support organisational sustainability. A qualitative literature review was conducted to analyse peer-reviewed articles, policy documents, and case studies. Thematic analysis was used to identify patterns and construct a conceptual framework illustrating the interdependence of VBAS and VBC. Findings indicate that administrative functions such as claims processing, fraud detection, and performance-based contracting are essential to achieving VBC objectives. Misaligned or inefficient administrative processes can compromise patient care, while well-structured VBAS systems support transparency, regulatory compliance, and cost control. VBAS enables VBC, transforming administrative functions from transactional support roles into strategic mechanisms for delivering value. The proposed framework offers healthcare leaders a practical model for aligning administrative and clinical strategies to achieve high-quality, patient-centred, and financially sustainable care.
Enhancing Decision Quality and Transparency via Machine Learning-Based Goodwill Impairment Estimation in Banks Wibisono, Gunawan; Nikhlis, Neilin; Wicaksono, Yosep Aditya; Faradila, Silvia
Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i3.233

Abstract

Goodwill impairment assessment remains a judgment-intensive process in banking institutions, where managerial discretion, information asymmetry, and regulatory complexity often challenge the quality of decisions and transparency. While prior studies have widely applied machine learning to financial risk assessment and credit analytics, they have paid limited attention to its role in improving managerial accountability in goodwill impairment decisions. This study aims to address this gap by developing and evaluating a machine-learning–based estimation framework to enhance the quality of decisions and transparency in bank-level goodwill impairment assessments. Using simulation-based analysis on synthetic financial statements, the proposed framework evaluates the performance of impairment estimation using quantitative metrics that capture predictive accuracy, decision consistency, and traceability. The findings demonstrate that ML-assisted estimation can systematically improve decision quality while strengthening transparency and accountability compared to traditional judgment-driven approaches. Beyond technical performance, the results indicate that machine learning can function as a governance-supporting mechanism by enabling more traceable and internally auditable impairment decisions. The study contributes theoretically by operationalizing transparency and accountability as measurable decision outcomes in corporate finance, and practically by offering banks a simulation-based tool for internal evaluation that does not rely on field experiments or sensitive proprietary data. Overall, the research highlights the potential of ML-enabled decision support systems to enhance both the quality and governance of goodwill impairment practices in the banking sector.
Supporting Strategic Intuition for Product Feature Innovation in Early-Stage Fintech Payments Start-ups Handoko, Sri; Qosidah, Nanik
Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i3.234

Abstract

Early-stage fintech payments start-ups face high uncertainty, limited historical data, and compressed decision cycles, making product feature innovation both critical and fragile. Despite growing attention to AI-supported tools and data-driven strategies, little is known about how strategic intuition guides product decisions in these contexts. This study develops a conceptual and practice-based framework to explore how strategic intuition, supported by digital leadership and human–AI collaboration, shapes feature ideation, prototyping, and prioritization processes. Using simulated product decision scenarios and data dummy analysis, the research maps decision points across development stages and examines how teams integrate intuitive judgment with analytical cues. Findings reveal that strategic intuition functions as a central mechanism for aligning feature choices with strategic goals, enhancing coherence and adaptability under uncertainty. Digital leadership legitimizes intuitive decisions, fosters cross-functional collaboration, and creates a psychologically safe environment, while AI tools complement rather than replace human judgment. The study contributes theoretically by positioning strategic intuition as a core element of product feature innovation in early-stage ventures and by integrating cognitive, social, and technological mechanisms into a unified framework. In practice, the framework provides actionable guidance for start-up teams to improve decision quality and speed without relying on costly field experiments, offering insights for managers, incubators, and policymakers seeking to support innovation under constraints. Overall, the research underscores the value of structured intuition as a deliberate, analytically informed process that advances understanding of cognition-supported innovation in nascent digital ventures.
Digital Gambling Marketing and Consumer Behaviour in South Africa: Insights from the Digital Gambling Influence Framework Willie, Michael Mncedisi
Journal of Management and Informatics Vol. 5 No. 1 (2026): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v5i1.307

Abstract

This study investigates the impact of digital gambling marketing on consumer behaviour, emphasising the interplay among AI-driven personalisation, social media influence, cognitive biases, ethical considerations, and regulatory governance. A narrative literature review was conducted to synthesise evidence on how digital marketing strategies shape consumer engagement, normalise gambling behaviours, and amplify risks among vulnerable populations. Complementing this, a case study of South Africa analysed recent shifts in gambling trends and the sector’s evolving dynamics. Findings reveal that the South African gambling industry has transitioned from traditional casino-based revenue to predominantly digital and mobile betting, illustrating how technological transformation drives consumer behaviour and industry growth. The literature indicates that algorithmic targeting and influencer-led promotions intensify exposure by exploiting cognitive biases, such as the illusion of control and reward anticipation. At the same time, gaps in regulatory oversight and inconsistent enforcement exacerbate potential harm. The study introduces the Digital Gambling Influence Framework (DGIF), a novel conceptual model that integrates marketing stimuli, user vulnerability, ethical boundaries, and governance as interdependent factors shaping individual and societal outcomes. The DGIF offers a theoretical contribution by bridging consumer behaviour, digital ethics, and regulatory governance, providing a structured lens for understanding the socio-technical dynamics of digital gambling. Practical implications include the need for adaptive regulations, responsible marketing practices, and empirical validation of conceptual frameworks to mitigate harm while sustaining consumer engagement.
Explainable AI-Driven Strategic Decision-Making in SMEs: Simulation-Based Evaluation of Ethical Governance Benjamin, Noah; Yulianingsih, Sri; Marie, Isabella
Journal of Management and Informatics Vol. 5 No. 1 (2026): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v3i1.314

Abstract

Given resource constraints and competitive pressures, we would have expected most SMEs to focus on the performance of AI over ethics. Our findings, however, ran squarely against those expectations and forced us to revise our assumptions about technological adoption in the smaller enterprise. Digital transformation in SMEs is not just about technology adoption; it is about trust building and organizational learning. While AI affords significant advantages in terms of competitiveness, the "black-box" nature of AI generates accountability gaps in ways that hit small businesses harder because they have limited capacity to absorb risk. Our study illustrates precisely how the integration of Explainable AI with digital ethics shifts decision quality in unexpected ways, to the benefit of both ethical compliance and business performance. Drawing on advanced simulation modeling and realistic synthetic data that represents SME scenarios, we compared three competing approaches: pure black-box AI, XAI without ethical safeguards, and XAI with full ethical integration. We were surprised by how the integrated approach improved not only ethical metrics but also improved strategic outcomes along many dimensions, such as in fairness, transparency, and decision quality. We provide a practical, evidence-based framework that guides SMEs through AI adoption via safe simulation environments, thereby avoiding expensive mistakes in the real world while systematically fostering stakeholder trust and organizational capability.
Corporate-Governance-Driven Algorithmic Fairness in SME Fintech Lending: A Systematic Literature Review with Expert Validation Victoria, Chloe; Müller, Daniel
Journal of Management and Informatics Vol. 5 No. 1 (2026): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v5i1.320

Abstract

The rapid growth of fintech lending has reshaped financial access for SMEs through AI-driven credit assessment platforms. While promising greater efficiency, these systems create significant algorithmic bias risks, which poor corporate governance and lack of transparency in model development usually exacerbate. Based on this, the study develops and validates an integrated conceptual framework that incorporates corporate governance principles with mechanisms for algorithmic fairness to foster ethical outcomes in SME fintech lending. We follow a two-phase approach, wherein, first, an SLR of 45 peer-reviewed publications for the period from 2022 to 2025 was conducted, followed by structured validation with five domain experts in AI ethics, corporate governance, and fintech regulation. Our analysis revealed four foundational governance pillars, viz., Accountability, Transparency, Fairness, and Compliance. Expert validation established strong relevance and practical utility for the framework, with a mean score of 4.6/5. This study hence proposes a novel, validated model to equip fintech managers and regulators with a governance-based approach to tackling algorithmic bias and, in turn, positions them better to engender trust and financial inclusion.
Blockchain-Based Supply Chain Transparency for Agricultural Produce S., Noorul Hassan; S., Sivaranjani; S., Sudha; Shree K., Jaya; R., Rajeswari
Journal of Management and Informatics Vol. 5 No. 1 (2026): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v5i1.329

Abstract

Food safety and economic growth depend on agricultural supply chains, yet these systems frequently struggle with fraud, inefficiency, and a lack of transparency.  Because traditional centralized systems struggle with traceability and stakeholder trust, it is challenging to ensure the quality and authenticity of produce.  Although modern technologies like cloud platforms and the Internet of Things (IoT) offer partial solutions, they remain susceptible to data manipulation and interoperability issues.  Blockchain offers a potential alternative for improving efficiency, accountability, and trust due to its decentralized and tamper-resistant nature. Creating a blockchain-based framework to improve transparency in agricultural product supply chains is the main goal of this project. This framework enables real-time tracking of products from farm to consumer, ensuring data integrity at every stage. It enhances coordination among farmers, distributors, retailers, and regulators through secure and transparent information sharing. Ultimately, the proposed system aims to reduce fraud, minimize losses, and strengthen consumer confidence in agricultural products.
Hybrid BiLSTM-Autoencoder Framework with Federated Learning for Intelligent Credit Card Fraud Detection S., Nafeeza; S., Shamataj; S., Hansika; S., Karthikeyan
Journal of Management and Informatics Vol. 5 No. 1 (2026): April Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v5i1.330

Abstract

The rapid expansion of digital payment systems has significantly increased the complexity and volume of financial transactions, leading to more sophisticated credit card fraud patterns that are difficult to detect using conventional approaches. This study proposes a hybrid fraud detection framework that integrates Bidirectional Long Short-Term Memory (BiLSTM), Autoencoder, and Federated Learning (FL) to enhance detection performance while preserving data privacy. The BiLSTM component captures temporal dependencies in transaction sequences by analyzing user behavior in both directions, enabling more accurate identification of irregular patterns. The autoencoder module functions as an unsupervised anomaly detector by learning representations of normal transactions and identifying deviations through reconstruction errors. To address data privacy constraints, the proposed model is deployed within a federated learning environment, allowing multiple institutions to collaboratively train a global model without sharing sensitive customer data. Experimental evaluation on benchmark datasets demonstrates that the proposed framework achieves superior performance over traditional machine learning and standalone deep learning models, particularly in precision, recall, and overall classification stability. The model effectively handles class imbalance and detects both known and previously unseen fraud patterns. Furthermore, the integration of federated learning enhances generalization by leveraging distributed data sources while maintaining strict confidentiality. This study contributes a scalable, privacy-preserving, and high-accuracy solution for real-world financial fraud detection, supporting secure collaboration across institutions and aligning with modern regulatory requirements.

Page 8 of 9 | Total Record : 83

 5   6   7   8   9 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol. 5 No. 1 (2026): April Season | JMI: Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics Vol. 4 No. 2 (2025): August Season | JMI : Journal of Management and Informatics Vol. 4 No. 1 (2025): April Season | JMI: Journal of Management and Informatics Vol. 3 No. 1 (2024): June: Journal of Management and Informatics Vol. 3 No. 1 (2024): April Season| JMI: Journal of Management and Informatics Vol. 2 No. 3 (2023): December : Journal of Management and Informatics Vol. 2 No. 1 (2023): April : Journal of Management and Informatics Vol 2 No 1 (2023): April : Journal of management and informatics Vol. 1 No. 3 (2022): December : Journal of Management and Informatics Vol 1 No 3 (2022): Desember : Journal of management and informatics Vol 1 No 2 (2022): Agustus: Journal of management and informatics Vol 1 No 1 (2022): April : Journal of management and informatics Vol. 1 No. 1 (2022): April : Journal of Management and Informatic More Issue