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
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
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
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
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
A New Theoretical Framework For Analyzing The Social And Economic Impacts Of Artifical Intelligence Within The Digital Economy
The use of artificial intelligence (AI) in the digital economy has drastically changed business processes and public services. However, in another way, this technology further exacerbates social and economic disparities. Disadvantaged groups such as low-skilled workers, micro and small enterprises (MSMEs), and groups less accessible to digital technology are largely not equally endowed with AI technology and digital infrastructure. This research aims to establish a new theoretical framework for comprehending the social and economic impacts of AI uptake in the digital economy, particularly on vulnerable groups. Employing a qualitative case study approach, this study is literature-reviewed and document-analyzed and based on five sociological theories at its essence: Social Stratification, Social Inequality, Social and Cultural Capital, Modern Stratification, and Network Society. The results of the study show that utilization of AI works to benefit individuals or groups with digital literacy skills and technological access, while reinforcing marginalization of those with fewer resources. This situation amplifies inherent structural inequality and creates a new layer in the form of digital stratification. The conceptual framework derived from this study presents an integrated and multi-disciplinary way of comprehending the far-reaching social implications of AI implementation. Apart from identifying the potential setbacks of technological exclusion, this research is also a springboard upon which to design more equitable and inclusive AI policy. By connecting classic sociological theory to present-day digital dynamics, this research presents a new contribution in the guise of analytical tools for assessing justice and inclusion in an AI economy.
Micro, Small, and Medium Enterprises (MSMEs)
digital economy, systematic literature review
Copyright (c) 2025 Anis Oktavia, Agus Wibowo https://creativecommons.org/licenses/by-sa/4.0
Supporting Strategic Intuition for Product Feature Innovation in Early-Stage Fintech Payments Start-ups
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.
Copyright (c) 2025 Sri Handoko, Nanik Qosidah https://creativecommons.org/licenses/by-sa/4.0
Enhancing Decision Quality and Transparency via Machine Learning-Based Goodwill Impairment Estimation in Banks
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.
Copyright (c) 2025 Gunawan Wibisono, Neilin Nikhlis, Yosep Aditya Wicaksono, Silvia Faradila https://creativecommons.org/licenses/by-sa/4.0
The Effect of Environmental Costs and Profitability on Environmental Performance
This research aims to analyze the influence of environmental costs and profitability on ecological performance at companies in Indonesia. This study is motivated by the phenomenon where many companies allocate ecological costs, but their effectiveness in improving environmental performance is still questionable. By using the method Weighted Least Squares (WLS) to overcome heteroscedasticity, this research tests the relationship between these variables using secondary data from company financial reports. The research results show that Environmental Cost does not have a significant effect on Environmental Performance, indicating that environmental spending is often used as a legitimation tool rather than as a real sustainability strategy. ROE has a significant negative effect on Environmental Performance, which shows that companies with high profitability focus more on achieving profits than on environmental investments. On the contrary, Firm Size has a significant positive effect on Environmental Performance, asserting that large companies are better able to implement environmental policies because they have more resources and face greater stakeholder pressure. This research provides implications for regulators to increase transparency in environmental spending as well as for investors in considering sustainability aspects in their investment decisions.
Copyright (c) 2025 Ratnaningrum , Dedi Hermawan, Bambang Widjanarko Susilo, Zahra Dinul Khaq https://creativecommons.org/licenses/by-sa/4.0
Determination of Employee Performance: Work Environment and Leadership Style : (Case Study at PT. MPIW Jakarta)
In today’s dynamic industrial environment, optimizing employee performance has become a strategic imperative for organizational sustainability. Among the factors that significantly influence workforce outcomes, the physical work environment and leadership style stand out as pivotal drivers. This study explores how these two variables affect employee performance within PT. MPIW, a prominent manufacturing firm in Jakarta. Employing a quantitative approach, data were obtained through structured questionnaires from 50 purposively selected respondents working in logistics, production, and purchasing divisions. Using IBM SPSS Statistics 21, the analysis revealed compelling insights. The work environment demonstrated a strong and positive influence on employee performance (t = 11.883, p < 0.001), affirming that factors such as spatial adequacy, safety, and interpersonal dynamics play a crucial role in enhancing productivity. Surprisingly, leadership style exerted a negative and statistically significant effect (t = -3.880, p < 0.001), suggesting that the prevailing authoritarian tendencies may undermine employee morale and engagement. The joint influence of both variables was also found to be significant (F = 86.518, p < 0.001), underscoring their combined relevance in shaping performance outcomes. This study contributes a critical perspective on organizational behavior in the manufacturing sector by integrating environmental and managerial dimensions into a unified analytical model. The findings underscore the need for adaptive, participatory leadership approaches and strategic enhancement of workplace conditions to drive sustainable performance improvements.
Copyright (c) 2025 Faizal Roni , Melyani Handoko, Rd Bily Parancika, Mohammad Aris, Yahya Mara Ardi, Syabrinildi https://creativecommons.org/licenses/by-sa/4.0
Implementation of Expert System Applications using Forward Chaining to Detect Dental Health
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.
Copyright (c) 2025 Zahra Zahra , Melyani Melyani, Faif Yusuf, Metty Titin Herawati, Suci Royanti, Athiy Dina RosihanaRosihana https://creativecommons.org/licenses/by-sa/4.0
Framework Analysis of Smart House based on Orange Technology use Systematic Literature
Now days, the home environment is still much less supportive of life for the elderly, most elderly living at home need a companion to help them. Safety, health, happiness and independence will be difficult for them to get even the elderly will be further away from the surrounding environment. Based on population projection 2017 data there are 23.66 million elderly people in Indonesia and predicted in 2020 there are 27.08 million people elderly. Smart homes that are currently widely studied have not focused on elderly; most smart homes only provide a sense of security and convenience for adult residents. And this will be a problem that until now has not solved the improvement of human life through technology to get happiness, care and health especially for the elderly. In many cases of the elderly, it is easier to send them to live in a nursing home and that keeps them separated from their families for the rest of their lives. That's what makes them less happiness. Orange Technology is a collection of technological elements to improve human life by getting happiness, Care and health. This study reviewed the journals of scientific databases such as IEEE explore, ACM digital library and Proquest published from 2002 to 2017. From the search results obtained 54 papers that will answer the scientific questions of this research. The result of this research is a framework of smart house that has Sensor, Monitoring, Wireless, Scalability, Low cost, GPS and ease of installation and maintenance as components of smart house of orange technology for elderly.
Copyright (c) 2025 A. Sudrajat, Melyani Handoko, Zahra, Hendra Kurniawan, Didin Solehudin, Dian Indah Sari, Fazhar Sumantri https://creativecommons.org/licenses/by-sa/4.0
Analysis of Price and Product Perceived Bolster Purchase Decision: Study Case in Fast Food Restaurant in East Bekasi Area
The increasing competition among fast food restaurants in urban areas has made consumers more selective in their purchasing decisions, with price and product quality emerging as key determinants. This study aims to analyze the influence of perceived price and perceived product quality on consumer purchasing decisions at Subway fast food restaurants in East Bekasi. A quantitative approach was applied using a structured questionnaire distributed to 50 consumers. Data were analyzed through multiple linear regression and classical assumption testing. The results indicate that price has a significant and positive effect on purchasing decisions, with a regression coefficient of 1.191 and a partial determination coefficient (R²) of 0.750, suggesting that price alone explains 75% of the decision variance. Similarly, perceived product quality also demonstrates a significant positive effect with a coefficient of 0.469 and R² of 0.724. When combined, both variables explain 72.4% of the variance in purchasing decisions, with the F-test result of 91.673 surpassing the critical value of 3.20. These findings suggest that consumers in East Bekasi prioritize both affordability and quality when choosing fast food. This research contributes to the literature by validating the dual importance of economic and experiential value in fast food marketing strategies, especially in highly competitive suburban environments.
Copyright (c) 2025 Indra Riyana Rahadjeng , Melyani Handoko, Wahyu Indrarti, Rizkiana Karmelia Shaura, Ahamd Rafik, Daniel, Isyana Emita, Dian Mohamad Anwar https://creativecommons.org/licenses/by-sa/4.0