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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 91 Documents
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The Application of AJAX Technology in Web-Based Information Systems to Accelerate Decision Making (Case Study: CV. Suka Mandi) Indra Riyana Rahadjeng; Melyani Handoko; Syabrinildi Syabrinildi; R Jatinurcahyo; Fazhar Sumantri; Didin Solehudin
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.v5i1.334

Abstract

This study evaluates the impact of AJAX technology on managerial decision-making performance in a web-based information system at CV. Suka Mandi. Unlike prior studies that focus primarily on system development, this research incorporates an empirical evaluation using a before–and–after comparison approach. The system was developed using the Waterfall model and integrated with AJAX technology to enhance responsiveness and usability. Data were collected through task completion time measurements, the System Usability Scale (SUS), and user performance evaluations. The results show a significant improvement in operational efficiency. The average task completion time decreased from 15.2 minutes to 8.1 minutes (a 46.7% improvement). The task success rate increased from 68% to 92%, while the error rate decreased from 22% to 8%. The usability evaluation produced a SUS score of 82.5, indicating excellent usability. A paired sample t-test confirmed that the improvements are statistically significant (p < 0.05). These findings demonstrate that AJAX technology not only enhances system interactivity but also significantly improves decision-making speed and accuracy. This study contributes by providing empirical evidence that links asynchronous web technology to improvements in managerial performance.
The Effect of Compensation on Employee Performance Through Work Motivation as an Intermediary Variable (Case Study at Archa Beauty Clinic Bekasi) Rizkiana Karmelia Shaura; Melyani Handoko; Reni Widyastuti; Rahayu Swastika; Diana Tambunan; Desy Tri Anggarini; Hendra Kurniawan
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.v5i1.335

Abstract

The rapid growth of digital payment systems has increased the complexity of financial transactions, making credit card fraud detection more challenging, particularly due to evolving fraud patterns and highly imbalanced datasets. Conventional machine learning approaches often struggle to capture temporal dependencies and adapt to new fraud behaviors, while centralized data processing raises privacy concerns. This study proposes a hybrid fraud detection framework that integrates Bidirectional Long Short-Term Memory (BiLSTM), Autoencoder, and Federated Learning to improve detection performance while preserving data confidentiality. The BiLSTM component models sequential transaction behavior from both forward and backward directions, while the autoencoder identifies anomalies based on reconstruction errors. Federated Learning enables collaborative model training across multiple institutions without sharing sensitive data. Experimental evaluation using benchmark datasets shows that the proposed model achieves high classification performance, with improved precision, recall, and overall stability compared to traditional and standalone deep learning models. The framework effectively handles class imbalance and detects both known and emerging fraud patterns. This study contributes a scalable and privacy-preserving solution for real-world fraud detection, supporting secure collaboration and enhancing model generalization in distributed financial environments.
IT Project Governance Maturity as a Predictor of Delivery Performance in Public Universities Pierre Dubois; Camille Laurent
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.v5i1.340

Abstract

his study examines the predictive role of IT project governance maturity on delivery performance in public universities, a context that has received limited empirical attention. Despite the critical importance of governance in higher education IT projects, few studies have quantitatively explored how maturity levels influence cost, schedule, and quality outcomes. The research objective is to analyze the extent to which governance maturity predicts delivery performance, providing a conceptual and empirical foundation for optimizing IT project outcomes. A non-experimental, quantitative approach was adopted, primarily simulation-based data designed to approximate institutional project conditions, incorporating variables such as project size, complexity, and performance indicators. Governance maturity was assessed using established frameworks such as COBIT and PMMM, while inferential analysis employed multiple regression and path analysis to evaluate predictive relationships. The findings indicate that governance maturity significantly predicts delivery performance, with higher maturity levels associated with improved project outcomes, while project complexity negatively affects performance in institutions with lower governance capability. Project size showed no significant effect, highlighting governance quality as the primary determinant of delivery success. These results offer practical implications for university IT managers, suggesting that investment in structured policies, formal monitoring mechanisms, and clear decision authority can enhance project outcomes. The study provides a preliminary predictive model that can support data-informed decision-making and serve as a reference point for future research in higher education IT governance. Findings should be interpreted as exploratory due to the use of simulation-based data.
Profitability of New Entrants in Capacity-constrained Oligopolies: A Data-driven Analysis Savanam Chandra Sekhar
Journal of Management and Informatics Vol. 5 No. 2 (2026): August 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.v5i2.352

Abstract

This study examines the profitability of new entrants in capacity-constrained oligopolistic markets using a data-driven empirical approach. Drawing on firm-level panel data across multiple industries over a 10–15-year period, the analysis integrates industrial organization theory with econometric modeling to assess how capacity conditions influence entry outcomes. The study focuses on key determinants, including incumbent capacity utilization, incumbent excess capacity, entrant capacity constraints, and market demand growth, while accounting for firm-specific and market-level controls. A fixed effects panel regression model, supplemented by robustness checks and alternative specifications, employed to examine the relationships between capacity conditions and entrant profitability while controlling for firm-specific and time-specific heterogeneity. The empirical results indicate that incumbent capacity utilization positively affects entrant profitability, while excess capacity serves as a strategic deterrent, reducing entrant performance. Entrant capacity constraints significantly hinder profitability, and interaction effects indicate that entrants must possess sufficient capacity to benefit from incumbent limitations. Market demand growth further enhances profitability, highlighting the role of external conditions. The study contributes to the literature by establishing capacity as a dynamic and strategic variable in entry analysis, offering implications for theory, managerial decision-making, and competition policy.
An Assistive Technology Framework for SDGs-Based Educational Games for Elementary School Children with Physical Disabilities Muhammad Perwiranegara; Melyani Handoko; Miranti Handayani; Windu Tiastuti; Faif Yusuf; Suci Riyanti
Journal of Management and Informatics Vol. 5 No. 2 (2026): August 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.v5i2.357

Abstract

The Sustainable Development Goals (SDGs) emphasize inclusive and equitable quality education for all learners. However, children with physical disabilities frequently encounter barriers when accessing digital educational resources, particularly educational games that lack accessibility features. This study proposes a Framework of Assistive Tools integrated into SDGs-based educational games to improve learning accessibility and achievement among elementary school children with physical disabilities. The framework consists of four interconnected layers: SDGs Layer, Educational Game Layer, Accessibility Layer, and Assistive Tools Layer. A quasi-experimental approach involving 30 elementary school students with physical disabilities was employed to evaluate the framework. Learning achievement, accessibility, engagement, and usability were assessed through pre-test and post-test evaluations. Simulated results indicate a significant increase in learning achievement from 63.4 to 77.5, representing a 22.2% improvement. Structural Equation Modeling using Partial Least Squares (PLS-SEM) demonstrated positive relationships among assistive tools, accessibility, educational game effectiveness, and learning achievement. The findings suggest that integrating assistive technologies into educational games contributes significantly to SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities). The proposed framework can serve as a guideline for future development of inclusive educational technologies.
Scenario Simulation of Ethical Trade-Offs in Executive Financial Strategy Issabell Zhang; Christina Tan
Journal of Management and Informatics Vol. 5 No. 2 (2026): August 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.v5i2.319

Abstract

The integration of artificial intelligence into executive financial decision-making has introduced new ethical challenges, creating complex trade-offs between financial optimality and ethical considerations. Current methodologies remain inadequate for systematically quantifying these dilemmas in strategic financial contexts. This study introduces a novel scenario-based simulation framework to measure and analyze trade-offs between financial performance and ethical consequences in AI-augmented decision environments. The research employs computational simulation modeling across ten strategic financial scenarios, involving configured AI agents and behaviorally profiled human agents executing 1,000 iterations per scenario under controlled conditions. The results indicate that ethical trade-offs are highly context-dependent, with 40% of scenarios showing a statistically significant negative correlation between financial and ethical outcomes. In high-conflict scenarios, AI-driven ethical considerations improve ethical scores by 34% while incurring an 18% opportunity cost in financial performance. Executive behavioral biases, particularly overconfidence and loss aversion, significantly degrade ethical outcomes beyond the improvements achieved through AI ethical calibration. Methodologically, this study contributes a replicable and extensible simulation-based approach for systematically quantifying ethical–financial trade-offs under controlled yet behaviorally grounded conditions. Practically, the findings provide actionable managerial and governance implications by informing the design of ethical audit mechanisms, bias mitigation strategies, and AI calibration policies in financial decision-making. The framework enables executives and regulators to conduct proactive ethical audits of AI systems, bridging the gap between theoretical AI ethics and practical financial governance.
When AI Policies Fail in Practice: Shadow AI as a Structural Policy–Practice Governance Misalignment Mia Wilson; Ethan Moore
Journal of Management and Informatics Vol. 5 No. 2 (2026): August 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.v5i2.327

Abstract

The widespread adoption of Artificial Intelligence (AI) has led organizations to establish formal governance frameworks aimed at mitigating ethical, legal, and operational risks. Despite these efforts, AI governance frequently fails in practice, as evidenced by the growing prevalence of Shadow AI the unsanctioned use of AI tools by employees. Existing scholarly and practitioner discourses predominantly frame this phenomenon as a compliance failure or security vulnerability, thereby emphasizing stricter controls and enhanced employee training as primary remedies. This conceptual study challenges that prevailing view by arguing that Shadow AI represents a structural manifestation of policy–practice misalignment rather than a problem of individual deviance. The study develops a diagnostic framework that identifies three constitutive dimensions of misalignment: temporal gaps (mismatches between governance processes and operational speed), utility gaps (misalignment between sanctioned tools and task-specific needs), and autonomy–control gaps (tensions between professional discretion and standardization). Drawing on a theory-driven conceptual methodology integrating sociotechnical systems theory with policy–practice analysis, and illustrated through structured synthetic organizational scenarios, the study demonstrates how governance designs that overlook the realities of situated work systematically generate Shadow AI practices. The analysis further suggests that adaptive governance models incorporating structured flexibility such as curated AI tool marketplaces and expedited approval pathways are theoretically more effective than highly rigid governance regimes. The primary contribution lies in advancing a practice-aware AI governance model that reframes Shadow AI as a diagnostic signal of systemic design flaws and provides a foundation for more legitimate and responsive AI governance.
Integrative Ethical Algorithmic Monitoring in Food Delivery: A Framework for Enhanced Managerial Decision-Making Isabella Martinez; Ryan Tan
Journal of Management and Informatics Vol. 5 No. 2 (2026): August 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.v5i2.328

Abstract

The rapid expansion of food delivery platforms, governed by opaque algorithmic systems, presents a critical managerial challenge: balancing relentless operational efficiency with fundamental ethical imperatives like fairness, transparency, and accountability. To address this, this study designs and evaluates a novel Integrative Ethical Algorithmic Monitoring (IEAM) framework aimed at enhancing the quality of managerial decision-making. Employing a rigorous design science research approach, our methodology integrates conceptual synthesis from algorithmic management and ethical AI literatures with scenario-based simulations. These simulations utilize structured dummy data modeling a mid-sized platform's operations 50,000 orders and 500 drivers across diverse zones to test the framework’s impact on key decision scenarios such as surge allocation and rating disputes. Key results demonstrate that the IEAM framework, which operationalizes ethics into dashboard metrics, significantly improves ethical outcomes. It reduced simulated order assignment disparity from 20% to 7% and increased the justified overturn rate for disputed algorithmic penalties by 40%. While marginal trade-offs in delivery time and processing costs were observed, the framework consistently shifted decisions toward greater equity. Its primary contribution is a validated, pragmatic tool that bridges abstract ethical AI principles with the daily realities of platform management. This enables more legitimate, sustainable, and data-driven governance, offering managers a proactive mechanism for responsible algorithmic oversight.
Instagram Sentiment Analysis for Customer Engagement Improvement in SMEs Carlos Mendes; Ana Silva
Journal of Management and Informatics Vol. 5 No. 2 (2026): August 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.v5i2.337

Abstract

Small and Medium Enterprises (SMEs) increasingly rely on Instagram as a primary digital marketing channel; however, customer engagement is frequently evaluated through quantitative metrics such as likes and reach, with limited integration of emotional orientation embedded in user-generated content. This study addresses this gap by positioning sentiment analysis as an evaluative tool within digital marketing analytics for SMEs. The research aims to identify customer sentiment patterns in SME Instagram content, explain the role of sentiment analysis in assessing customer engagement, and provide a data-driven foundation for improving engagement performance. The study adopts a quantitative design using naturally occurring Instagram data, including captions and comments collected from SME accounts within a defined period. Text preprocessing is conducted prior to sentiment classification using a lexicon-based approach with the VADER (Valence Aware Dictionary and Sentiment Reasoner) model to capture polarity and intensity of sentiments in social media text. Furthermore, statistical analysis is performed using correlation analysis and one-way ANOVA to examine the relationship and differences between sentiment categories and engagement indicators (likes, comments, and interaction rate). The findings reveal systematic variation in engagement metrics across sentiment polarity categories, indicating that emotional orientation represents a relevant analytical dimension in engagement evaluation. The study contributes theoretically by reinforcing the multidimensional perspective of customer engagement and methodologically by integrating text mining techniques with engagement analytics in the SME context. Practically, it offers a replicable and scalable analytical framework that enables SMEs to incorporate sentiment-based evaluation into Instagram content strategy development.
Board Oversight Mechanisms in Financial Distress Decision-Making: A PRISMA-Based Systematic Review Andrei Popescu; Elena Ionescu
Journal of Management and Informatics Vol. 5 No. 2 (2026): August 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.v5i2.342

Abstract

Corporate governance plays a critical role in shaping organizational responses to financial distress, yet prior research has largely emphasized distress prediction rather than examining how boards actually exercise oversight during crisis decision-making. This study aims to clarify how board oversight mechanisms are conceptualized and how they influence strategic decision quality under conditions of financial pressure. Using a PRISMA-based systematic literature review, the study systematically identified, screened, and synthesized peer-reviewed governance and crisis management research, followed by thematic coding and conceptual mapping to classify oversight mechanisms across structural, procedural, and behavioral dimensions. The findings reveal that these three dimensions consistently appear across the literature and jointly shape decision accountability, responsiveness, and reliability, indicating that governance effectiveness emerges from their interaction rather than from any single mechanism. The review also identifies a persistent conceptual gap between bankruptcy prediction studies and governance decision-making scholarship, highlighting fragmentation in existing knowledge. These results contribute theoretically by proposing an integrated typology of oversight mechanisms tailored to distress contexts and practically by offering an evidence-based framework that organizations and regulators can use to evaluate board effectiveness during crisis periods. The study further demonstrates the value of systematic synthesis methods for advancing conceptual clarity in multidisciplinary governance research.

Page 9 of 10 | Total Record : 91

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