Journal Title: Journal of Management and Informatics Initials: JMI Frequency: April, August, December Print ISSN: 2961-7731 Online ISSN: 2961-7472 Editor in Chief : Dr. Ir. Agus Wibowo, M.Kom, M.Si, MM., DOI: 10.51903 Publisher: University of Science and Computer Technology The Journal of Management and Informatics (JMI) is a peer-reviewed academic and open access journal that publishes research at the intersection of management science, information systems, and data-driven technologies. The journal focuses on digital transformation, business analytics, artificial intelligence in management, and data-driven decision making, emphasizing how organizations leverage technology and data to enhance strategic and operational performance. JMI prioritizes interdisciplinary studies that integrate management perspectives with analytical methods, intelligent systems, and technology management. It is published three times a year (April, August, and December) and welcomes contributions from the international research community. Indexed by:
University of Science and Computer Technology
An Assistive Technology Framework for SDGs-Based Educational Games for Elementary School Children with Physical Disabilities
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.
Copyright (c) 2026 Muhammad Perwiranegara, Melyani Handoko, Miranti Handayani, Windu Tiastuti, Faif Yusuf, Suci Riyanti https://creativecommons.org/licenses/by-sa/4.0
Profitability of New Entrants in Capacity-constrained Oligopolies: A Data-driven Analysis
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.
Copyright (c) 2026 Savanam Chandra Sekhar https://creativecommons.org/licenses/by-sa/4.0
AI-Driven Multi-Criteria Decision Model for Prioritizing Strategic Corporate Social Responsibility Programs
Corporate Social Responsibility (CSR) programs have become increasingly important for organizations seeking to balance strategic objectives with societal needs, yet prioritizing initiatives remains challenging due to limited resources and multiple evaluation criteria. This study aims to develop an artificial intelligence-driven multi-criteria decision model to systematically prioritize strategic CSR programs based on their social and organizational impact. Simulated CSR program scenarios representing community education, public health, environmental sustainability, and local economic empowerment were generated, and each alternative was assessed across five criteria: social impact, strategic alignment, sustainability, stakeholder benefit, and cost efficiency. The model applied a multi-criteria decision-making approach integrated with artificial intelligence techniques to assign criterion weights, calculate aggregated scores, and rank CSR program alternatives. The results indicated that social impact and strategic alignment were the most prominent criteria, with mean scores ranging from 3.60 to 4.25 on a five-point scale, whereas cost efficiency contributed the least to prioritization outcomes. The proposed model demonstrated consistent analytical behavior and enabled structured evaluation of trade-offs among competing objectives. The study contributes theoretically by extending CSR research into data-driven decision-making frameworks and practically by providing organizations with a replicable approach for evaluating and selecting CSR programs that align with both strategic and social goals. These findings suggest that integrating artificial intelligence with multi-criteria decision-making can enhance transparency, consistency, and effectiveness in corporate social responsibility management.
Copyright (c) 2026 Dimitrios Papadopoulos, Eleni Nikolaou https://creativecommons.org/licenses/by-sa/4.0
Board Oversight Mechanisms in Financial Distress Decision-Making: A PRISMA-Based Systematic Review
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.
Copyright (c) 2026 Andrei Popescu, Elena Ionescu https://creativecommons.org/licenses/by-sa/4.0
IT Project Governance Maturity as a Predictor of Delivery Performance in Public Universities
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.
Copyright (c) 2026 Pierre Dubois, Camille Laurent https://creativecommons.org/licenses/by-sa/4.0
Instagram Sentiment Analysis for Customer Engagement Improvement in SMEs
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.
Copyright (c) 2026 Carlos Mendes, Ana Silva https://creativecommons.org/licenses/by-sa/4.0
The Effect of Compensation on Employee Performance Through Work Motivation as an Intermediary Variable (Case Study at Archa Beauty Clinic Bekasi)
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.
Copyright (c) 2026 Rizkiana Karmelia Shaura, Melyani Handoko, Reni Widyastuti, Rahayu Swastika, Diana Tambunan, Desy Tri Anggarini, Hendra Kurniawan https://creativecommons.org/licenses/by-sa/4.0
The Application of AJAX Technology in Web-Based Information Systems to Accelerate Decision Making (Case Study: CV. Suka Mandi)
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.
Copyright (c) 2026 Melyani Handoko, Syabrinildi Syabrinildi, Indra Riyana Rahadjeng, R Jatinurcahyo, Fazhar Sumantri, Didin Solehudin https://creativecommons.org/licenses/by-sa/4.0
Blockchain-Based Supply Chain Transparency for Agricultural Produce
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.
Copyright (c) 2026 Noorul Hassan S., Sivaranjani S., Sudha S., Jaya Shree K., Rajeswari R. https://creativecommons.org/licenses/by-sa/4.0
Hybrid BiLSTM-Autoencoder Framework with Federated Learning for Intelligent Credit Card Fraud Detection
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.
Copyright (c) 2026 Nafeeza S., Shamataj S., Hansika S., Karthikeyan S. https://creativecommons.org/licenses/by-sa/4.0
Integrative Ethical Algorithmic Monitoring in Food Delivery: A Framework for Enhanced Managerial Decision-Making
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.
Copyright (c) 2026 Isabella Martinez, Ryan Tan https://creativecommons.org/licenses/by-sa/4.0
When AI Policies Fail in Practice: Shadow AI as a Structural Policy–Practice Governance Misalignment
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.
Copyright (c) 2026 Mia Wilson, Ethan Moore https://creativecommons.org/licenses/by-sa/4.0
Corporate-Governance-Driven Algorithmic Fairness in SME Fintech Lending: A Systematic Literature Review with Expert Validation
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.
Copyright (c) 2026 Chloe Victoria, Daniel Müller https://creativecommons.org/licenses/by-sa/4.0
Scenario Simulation of Ethical Trade-Offs in Executive Financial Strategy
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.
Copyright (c) 2026 Issabell Zhang, Christina Tan https://creativecommons.org/licenses/by-sa/4.0
Explainable AI-Driven Strategic Decision-Making in SMEs: Simulation-Based Evaluation of Ethical Governance
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.
Explainable Artificial Intelligence (XAI)
Copyright (c) 2023 Noah Benjamin, Sri Yulianingsih, Isabella Marie https://creativecommons.org/licenses/by-sa/4.0
Value-Based Administration Services and Value-Based Care: Aligning Administrative Efficiency with Patient Outcomes
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.
Copyright (c) 2025 Michael Mncedisi Willie https://creativecommons.org/licenses/by-sa/4.0
Digital Gambling Marketing and Consumer Behaviour in South Africa: Insights from the Digital Gambling Influence Framework
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.
Copyright (c) 2026 Michael Mncedisi Willie https://creativecommons.org/licenses/by-sa/4.0
Exploring the Role of Digital Tools in Ethical Managerial Decision-Making
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.
Copyright (c) 2025 Miguel Álvarez, Leila Hassan https://creativecommons.org/licenses/by-sa/4.0
Human Error vs. System Security: Evaluating the Weakest Link in Digital Business Information Systems
The perennial question in digital business cybersecurity concerns whether human error or technical system vulnerabilities constitute the greater threat to organizational information systems and thus should receive priority in security investment. This study empirically examines this issue by identifying the weakest link in contemporary digital business environments. The study offers a theoretical contribution by integrating Human Error Theory, Socio-Technical Systems Theory, and the ISO 27001 framework into a unified analytical model for evaluating organizational information security weaknesses. Using an explanatory sequential mixed-methods design, quantitative data were collected from 217 information technology professionals, complemented by 15 in-depth interviews and an analysis of security incident records. The results indicate that human error (M = 3.82) is significantly more prevalent than technical system vulnerabilities (M = 2.94), as confirmed by a paired t-test (t(216) = 5.734, p < .001). Structural Equation Modeling further reveals that workload pressure and insufficient practice-based training significantly contribute to human error (β = 0.58, p < .001). Qualitative findings highlight cognitive overload, training gaps, and social engineering as dominant contributing factors. The study demonstrates that human error should not be interpreted merely as individual negligence but as an outcome of more profound organizational and socio-technical weaknesses. These findings support a strategic shift toward human-centered and socio-technical cybersecurity approaches to enhance organizational digital resilience.
Copyright (c) 2025 Nguyen Thị Mai, Iman Khalid https://creativecommons.org/licenses/by-sa/4.0
Uncovering Hidden Skill Gaps: Technology Bias in Gig Platforms
The rapid expansion of the digital gig economy, driven by transparent algorithmic regimes, has bequeathed a new frontier of re-formation of labor that warrants serious scrutiny. This study investigates how algorithmic bias shapes the dynamic skill formation of freelance workers an overlooked mechanism that influences employability and performance. Employing an explanatory sequential mixed-method design, quantitative questionnaires were administered to 342 gig workers on Upwork, Fiverr, and Gojek platforms and complemented with in-depth interviews of 25 participants. The results show a high positive correlation between perceived algorithmic bias and the dynamics of the demand for skills (β = 0.48, p < .001) which suggests that the higher the perceived bias, the greater the extent of required skillset changes among the workers. The changes are negatively correlated with perceived employability and performance (β = –0.31, p < .001). Qualitative data reveal three interdependent experiences: negotiating the "black box" of algorithmic control, the de-professionalization of vocational skills to secure "algorithmic mastery," and the emergence of adaptive, frequently collective, coping strategies. Synthesizing knowledge from Management Information Systems, Human Capital Theory, and the Technology Acceptance Model, this study constructs an evidence-based model for how algorithmic systems reorganize human capital. The. conclusion emphasizes the importance of transparent algorithmic design and participatory ability-building policy to yield fairness and sustainability in the digital platform of labor.
Copyright (c) 2025 Hiroko Tanaka, Ahmed El-Masry https://creativecommons.org/licenses/by-sa/4.0
The Role of Strategic Financial Planning in Enhancing Organizational Resilience: A Cross-Industry Perspective
This study examines the role of strategic financial planning in strengthening organizational resilience across industries. Using a mixed-methods research design, qualitative data were collected through in-depth interviews with financial managers, while quantitative data were obtained from corporate financial reports across multiple sectors. The integration of these two approaches enables a more comprehensive assessment of how financial strategies influence adaptive capacity, risk preparedness, and long-term organizational stability. The findings reveal that organizations implementing scenario-based planning, sustainability-oriented budgeting, and proactive capital allocation exhibit significantly greater resilience to economic uncertainty and market volatility. Moreover, financial sustainability is identified not only as a key driver but also as a strategic outcome of organizational resilience, reinforcing the interdependence between prudent financial management and adaptive performance. These results offer important managerial implications by demonstrating how strategic financial planning can be operationalized as a resilience-building mechanism across different industrial contexts. From a policy perspective, the study highlights the need for regulatory frameworks that encourage long-term financial sustainability as a foundation for organizational adaptability.
Copyright (c) 2025 Amir Farouq, Camila Rios https://creativecommons.org/licenses/by-sa/4.0
Investor Sentiment, Overconfidence, and Market Volatility: Insights from Global Stock Exchanges
The increasing turbulence in global stock markets has sparked growing attention toward the role of behavioral factors in shaping investor decisions. Traditional financial theories often assume rationality; however, evidence shows that psychological biases, such as investor sentiment and overconfidence, frequently disrupt market efficiency. This study investigates how investor sentiment and overconfidence bias influence market volatility across international stock exchanges, addressing gaps in prior research that often examine these factors in isolation. A quantitative research design was employed, combining a behavioral survey of active investors with secondary data from global stock indices from 2018 to 2023. The behavioral survey captured self-reported sentiment and overconfidence, while the secondary dataset provided market-level volatility indicators. Structural equation modeling (SEM) was applied to test the hypothesized relationships, with robustness checks conducted via regression analysis. Instrument reliability and validity were confirmed, ensuring measurement consistency. The findings reveal that both investor sentiment and overconfidence bias significantly drive fluctuations in market volatility, with overconfidence exerting a more substantial amplifying effect. Interactions between sentiment and overconfidence further intensify volatility during periods of global uncertainty, illustrating the complex and nonlinear nature of behavioral influences. These results align with behavioral finance theory, which posits that markets are not purely efficient but are shaped by human cognition and emotion. The study provides empirical insights from a cross-market perspective, bridging behavioral finance with international management research, and offers practical guidance for policymakers, regulators, and financial advisors in managing behavioral risks to enhance global market stability.
Copyright (c) 2025 Lina Maretta, Jonas Redwood https://creativecommons.org/licenses/by-sa/4.0
Enterprise Risk Management Practices and Their Impact on Firm Performance: Evidence from Multinational Corporations
Global business environments have become increasingly volatile, pushing multinational corporations (MNC) to seek more resilient strategies for sustaining long-term performance. Enterprise Risk Management (ERM) has emerged as a critical framework, yet its effectiveness often depends on the interplay of governance diversity, digital transformation, and cultural adaptability. This study aims to examine how ERM, supported by these contextual factors, influences the performance of MNC operating in dynamic institutional settings. The research employs a quantitative approach, using survey data collected from 110 senior managers across multinational firms. Data were analyzed through Structural Equation Modeling (SEM) to test direct and mediated relationships between ERM practices and firm performance. The model integrates governance diversity, digital transformation, and cultural adaptability as complementary enablers that shape the effectiveness of ERM implementation. The results reveal that ERM positively contributes to firm performance when organizations embrace diverse governance structures, leverage digital tools, and nurture adaptive organizational cultures. Governance diversity strengthens decision-making and oversight, digital transformation enhances risk anticipation, and cultural adaptability ensures contextual alignment across markets. Together, these factors create a synergistic environment that amplifies ERM role in driving performance. The findings extend theoretical discussions on ERM by situating it within broader organizational and institutional contexts. Practically, they underscore the importance for managers to integrate ERM into corporate strategy while fostering diversity and digital readiness. For policymakers, the study suggests harmonizing disclosure standards and strengthening institutional frameworks to enhance corporate resilience.
Copyright (c) 2025 Farah Nayme, Imaane Taybi https://creativecommons.org/licenses/by-sa/4.0
Global Corporate Financing Approaches and Investment Strategy Optimization: A Comparative Study of Emerging and Developed Markets
The dynamics of corporate financing strategies have become increasingly complex in an era marked by global uncertainty and diverse market structures. While developed markets often benefit from established institutional frameworks and sophisticated capital access, emerging markets face persistent challenges in striking a balance between sustainability, efficiency, and risk management. This study aims to compare corporate financing approaches across emerging and developed economies, focusing on their effectiveness in optimizing investment decisions and supporting long-term growth. Employing a mixed-method approach, the research combines quantitative analysis of financing performance indicators with qualitative case studies on institutional and governance contexts. The results demonstrate that firms in developed markets tend to rely on equity-based financing and shareholder-driven governance structures, which enhance transparency and facilitate portfolio optimization. Conversely, companies in emerging markets often depend more heavily on debt instruments, public-private partnerships, and adaptive strategies shaped by institutional limitations and behavioral investment patterns. A comparative analysis highlights these structural differences, providing insight into how financing decisions reflect both market maturity and contextual constraints. This study concludes that while developed markets achieve efficiency through established governance and diversified financing channels, emerging markets reveal innovation in addressing volatility and resource scarcity. The findings contribute to the discourse on comparative financial management by bridging theoretical frameworks with practical strategies, offering guidance for policymakers, corporate leaders, and investors seeking sustainable investment outcomes.
Copyright (c) 2025 Abioye Basel, Lamond Lethabo, Zola Tessema https://creativecommons.org/licenses/by-sa/4.0
Impact of Liquidity and Sales Growth on Company Value: A Case Study of Telecommunications Firms on the Indonesian Stock Exchange
This study investigates the impact of liquidity and sales growth on company value within the Indonesian telecommunications sector from 2020-2024. Grounded in Signaling Theory, this research employs a quantitative panel data regression on a saturated sample of five IDX-listed firms. Using the Price-to-Book Value ratio as a proxy for company value, the analysis tests the influence of the Current Ratio (liquidity) and annual sales growth. The selected Fixed Effects Model shows that both liquidity and sales growth have significant positive effects, confirming that they serve as credible signals influencing investor valuation.
Copyright (c) 2025 Indra Riyana Rahadjeng , Melyani Melyani, Dian Mohamad Anwar, Ahmad Rafik, Isyana Emita, Wahyu Indrarti, Dian Indah Sari https://creativecommons.org/licenses/by-sa/4.0
The Effect of Financial Compensation on Employee Performance : Study Case at Bank SMBC indonesia
This research investigates the effect of financial compensation on employee performance at Bank SMBC Indonesia. Using a quantitative approach, 120 employees were surveyed via a structured questionnaire. Data were analyzed using SPSS 26, including reliability testing, normality analysis, simple linear regression, ANOVA, and coefficient testing. The results show that financial compensation has a significant and positive effect on employee performance, with an R² of 0.479, meaning that compensation accounts for approximately 47.9% of the variance in performance. Suggestions for SMBC Indonesia include optimizing compensation mechanisms, such as performance-based bonuses, to enhance employee productivity.
Copyright (c) 2025 Daniel Daniel , Melyani Melyani, Syabrinildi Syabrinildi, Hendra Kurniawan, Fazhar Sumantri, Didin Solehudin, Hendra Lesmana https://creativecommons.org/licenses/by-sa/4.0
The Effect of Quality Product, Price Perception, and Promotion onVivo Smartphone Purchase Decisions: A Study at the Archa PhoneCounter in Bekasi
This study investigates the influence of product quality, price perception, and promotion on Vivo smartphone purchase decisions. A quantitative survey was administered to 100 consumers at the Archa Phone Counter in Bekasi using purposive sampling. Multiple linear regression analysis revealed that all three variables have a significant positive partial effect on the purchase decision. The model explained 85% of the variance (Adjusted R² = 0.726), confirming their simultaneous influence. Product quality was identified as the most dominant predictive factor, validating its critical role in consumer choice.
Copyright (c) 2025 Melyani Melyani, Rahayu Swastika, Reni Widyastuti, Rizkiana Karmelia Shaura, Eigis Yani Pramularso, Desy Tri Anggarini, Diana Tambunan https://creativecommons.org/licenses/by-sa/4.0
Analysis of Organizational Culture and Work Environment on Employee Performance : Case study at Duren Sawit District Office, East Jakarta
This study investigates the influence of organizational culture and work environment on employee performance at the Duren Sawit District Office. Anchored in Social Cognitive Theory, this quantitative research employed a census method, surveying all 85 employees. Data were analyzed using multiple linear regression. The results revealed that both organizational culture and work environment have a significant and positive partial and simultaneous effect on employee performance. The model explained 52.4% of the variance in performance, confirming that a conducive workplace context significantly predicts employee behavior.
Copyright (c) 2025 Henny Armaniah, Melyani Melyani, Andri Rizko Yulianto, Wasilatun Nikmah, Herudini Subariyanti, Yulianto Yulianto https://creativecommons.org/licenses/by-sa/4.0
Beyond Descriptive Analytics: Predictive Models For Strategic Marketing Decisions
As marketing ecosystems grow increasingly complex, organizations are under pressure to move beyond descriptive analytics and embrace predictive models to enhance strategic marketing decisions. This study investigates the application of predictive analytics, powered by machine learning, in optimizing marketing decision-making processes. Employing a quantitative research approach, we analyzed historical and behavioral data from a digital retail platform over a 12-month period. Several machine learning techniques logistic regression, random forest, and XGBoost were used to build predictive models that estimate customer conversion probabilities and forecast campaign outcomes. The results indicate that predictive models can significantly improve the precision of strategic marketing initiatives, enabling marketers to identify high-value customer segments and allocate resources more efficiently. Compared to traditional methods, the predictive approach led to a measurable uplift in campaign effectiveness and ROI. From a managerial perspective, the study highlights how data-driven strategy and real-time insights can support agile, evidence-based decision-making in competitive markets. Academically, this research contributes to the growing field of predictive marketing analytics by demonstrating the strategic utility of machine learning techniques. In an era where consumer behavior evolves rapidly, leveraging predictive tools is no longer optional it is essential.
Copyright (c) 2025 Nabila Putri, Aghisti Ainindhira https://creativecommons.org/licenses/by-sa/4.0
Exploring the Impact of Artificial Intelligence on Customer Experience Personalization and Marketing Strategy Optimization in Digital Marketing: An Empirical Analysis.
This study explores the transformative role of Artificial Intelligence (AI) in enhancing customer experience personalization (CEP) and optimizing marketing strategies (MSO) within digital marketing environments. Grounded in the Technology Acceptance Model (TAM), the research incorporates ethical and privacy concerns (EC) as moderating factors influencing AI adoption and its effectiveness. Using survey data from marketing professionals across diverse industries, the study employs Structural Equation Modeling (SEM) to analyze complex relationships among AI use, CEP, MSO, and marketing outcomes. The findings reveal that AI significantly improves customer engagement and marketing performance by enabling tailored interactions, data-driven segmentation, and campaign optimization. Moreover, ethical and privacy concerns positively moderate these effects, underscoring the necessity of responsible AI practices in sustaining consumer trust and regulatory compliance. By offering empirical insights into the interplay between AI, personalization, strategy, and ethics, this study contributes to both theoretical development and practical guidance for businesses navigating digital transformation. The research highlights AI’s strategic potential in creating sustainable competitive advantage while advocating for ethical safeguards in technology-driven marketing.
Copyright (c) 2025 Daniel Owusu-Mensah , Philip Adu Sarfo, Gideon Appiah Kusi https://creativecommons.org/licenses/by-sa/4.0