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International Journal for Applied Information Management
Published by Bright Institute
ISSN : -     EISSN : 27768007     DOI : https://doi.org/10.47738/ijaim
Core Subject : Economy, Humanities, Science, Social,
Humanities Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences
Journal menerbitkan penelitian tentang semua aspek manajemen informasi. Informasi dilihat di sini secara luas untuk mencakup tidak hanya produk/layanan dan proses tetapi juga pasar, dan organisasi serta informasi sosial. Ini termasuk studi tentang proses secara keseluruhan atau tahap individu, masalah seputar mengakses dan menggunakan sumber daya berwujud dan tidak berwujud secara efektif, strategi informasi, alat yang berbeda yang digunakan untuk mengelola informasi, dampak faktor industri, regional, dan nasional, dan implikasi pada kinerja. . IJAIM menyambut baik pekerjaan yang mengeksplorasi manajemen inovasi dalam konteks baru seperti tetapi tidak hanya layanan, organisasi sektor publik, dan perusahaan sosial dan komunitas, informasi sosial, pada satu atau beberapa tingkat termasuk tim atau proyek, organisasi, regional , nasional dan internasional. Makalah yang muncul di IJAIM harus didasarkan pada metode penelitian yang ketat. Mereka juga harus eksplisit tentang implikasi untuk teori dan praktek. Dengan demikian, penulis harus memastikan bahwa kontribusi terhadap keadaan seni diartikulasikan dengan jelas.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 149 Documents
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Investigating the Influence of Human-AI Collaboration on Electronic Word of Mouth Through Customer Satisfaction in Event Services Mark Joseph B. Marquez; Julien Albert Andal; Gabriel Molina; Maria Bianca Oliveros; Jayvie O. Guballo
International Journal for Applied Information Management Vol. 6 No. 1 (2026): Regular Issue: April 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v6i1.117

Abstract

High-touch events emphasize direct artist–fan interaction and have become an important driver of engagement and economic activity in the entertainment industry. However, limited research has examined how service quality in such events influences customer satisfaction and electronic word-of-mouth (e-WOM), particularly in the Philippine context. This study aims to investigate the relationships between high-touch event service quality, customer satisfaction, and e-WOM, as well as the mediating role of customer satisfaction. A quantitative research design was employed using survey data collected from 291 respondents who had attended high-touch events in Metro Manila. Data were analyzed using weighted mean, Pearson correlation, and mediation analysis. The results revealed that service quality has a strong positive relationship with customer satisfaction (r = 0.821, p < 0.05) and e-WOM (r = 0.835, p < 0.05). Customer satisfaction also significantly influences e-WOM (r = 0.786, p < 0.05) and partially mediates the relationship between service quality and e-WOM. These findings indicate that improving service quality in high-touch events enhances customer satisfaction, which in turn promotes positive e-WOM. The study highlights the importance of delivering high-quality interactive experiences to strengthen customer retention and engagement in the event industry.
Understanding Customer Recommendation Intentions in Human-AI Collaborative Smart Vending Services Through Perceived Satisfaction Aleen Miguel E. Bonquin; Allaiza Mariele C. Bataycan; Angelo Kian G. Bautista; Jan Calvin S. Bascones; Travis Jan B. De Guzman; Jayvie O. Guballo
International Journal for Applied Information Management Vol. 6 No. 1 (2026): Regular Issue: April 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v6i1.118

Abstract

This study aims to examine the mediating role of perceived satisfaction in the relationship between service quality and willingness to recommend in the context of a custom phone case vending machine. Despite the growing adoption of automated retail technologies, limited empirical studies have explored customer behavior in personalized vending services, particularly in emerging markets. Addressing this gap, the study employed a descriptive-correlational research design with 319 respondents from selected shopping malls (Robinsons Galleria, SM Fairview, and SM Aura). Data were analyzed using weighted mean, regression analysis, and Sobel test to assess both direct and indirect relationships among variables. The findings indicate that respondents reported high perceptions across all SERVQUAL dimensions (reliability, assurance, tangibles, empathy, and responsiveness), suggesting strong overall service performance. Perceived satisfaction (M = 4.36) and willingness to recommend (M = 4.38) were also rated highly, indicating positive customer evaluations and advocacy intentions. Mediation analysis revealed that perceived satisfaction partially mediates the relationship between service quality and willingness to recommend. This suggests that while service quality directly influences recommendation behavior, it also exerts an indirect effect through enhanced customer satisfaction. This study contributes to the extension of service quality and customer behavior models in automated retail contexts by demonstrating both direct and mediated effects of service quality on recommendation intention. Practically, the findings highlight the importance of maintaining high service standards and optimizing customer experience in vending-based retail systems to strengthen satisfaction and encourage positive word-of-mouth.
Human-AI Collaboration in Telemedicine and Knowledge Security A Review of Emerging Research Trends Mohammed Ahmed Alhebbi; Ibrahiem M. M. El Emary
International Journal for Applied Information Management Vol. 6 No. 1 (2026): Regular Issue: April 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v6i1.119

Abstract

Telemedicine has fundamentally transformed healthcare delivery by enabling remote consultations, diagnoses, and follow-ups, thereby enhancing accessibility and efficiency. Nevertheless, this transformation poses critical challenges related to data security, confidentiality, and the protection of knowledge exchange. This paper seeks to review the literature on knowledge security and its relationship with telemedicine within healthcare organizations. The paper summarizes trends of knowledge security and telemedicine, underscores their interconnections, and traces their evolution over time. It also reviewed the historical development of both concepts, examined them jointly and separately, and addressed their associated notions. Moreover, the study reviewed conferences and scientific societies that engaged with the topic, as well as research interests in the topic and their progression within contemporary intellectual production. Finally, a digital indicator was employed to trace the terminology related to the topic. The study used a descriptive–analytical method, which involved consulting the literature including research papers, articles, and books and subjecting it to both descriptive and analytical examination. This approach was applied to review the key terminology relevant to the topic and to extract related findings and implications. The study concluded from the literature that research interests have addressed knowledge security and information security on the one hand, and telemedicine on the other; however, these domains diverged in their approaches and variables. Furthermore, to the best of the researcher’s knowledge, this literature represents the first contribution in Arabic to link knowledge security with telemedicine explicitly. The researcher intends to expand on this connection in future work to make a distinctive contribution to intellectual production in this field.
Human-AI Collaboration in Knowledge Management and Its Role in Enhancing Institutional Performance Khalid Ali Alzahrani; Ibrahiem M. M. El Emary
International Journal for Applied Information Management Vol. 6 No. 1 (2026): Regular Issue: April 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v6i1.120

Abstract

This study reviews the literature on knowledge management (KM) and its role in improving institutional performance, with a focus on applications in institutional communication centers. It aims to assess the development of research in this field, identify key themes related to KM practices, and examine their impact on organizational performance, while outlining potential directions for future research. The study adopts an inductive analytical approach through the examination of relevant scientific publications to identify major trends and patterns in the literature. The review is contextualized within the Institutional Communication Center at King Abdulaziz University, Jeddah, during the academic year 1447 AH / 2025 AD, as a case reflecting the relationship between KM and institutional communication. The findings indicate that the effective implementation of KM practices contributes to improving institutional performance by enhancing service quality, supporting informed decision-making, and strengthening communication processes within the organization. In addition, integrating KM into communication environments supports the role of communication centers as key channels linking institutions with their stakeholders. The analysis also shows that digital transformation, particularly through social media and digital platforms, plays an important role in facilitating knowledge sharing, interaction, and innovation. Previous studies emphasize the importance of developing integrated KM strategies, enhancing employee capabilities, and promoting organizational flexibility to support sustained institutional performance. Overall, the study confirms that knowledge management represents an important approach for improving institutional performance, especially when aligned with communication functions and digital developments within organizations.
Decision Policy Optimization for Human–AI Collaboration Using Off-Policy Reinforcement Learning from Logged Interaction Data Hery; Ariel Christopher Wawolangi
International Journal for Applied Information Management Vol. 6 No. 2 (2026): Regular Issue: July 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v6i2.121

Abstract

 This paper investigates offline policy optimization for adaptive learning using logged student interaction traces, targeting reliable improvement without online exploration. A conservative offline reinforcement learning pipeline is implemented with calibrated behavior-policy propensities and doubly robust off-policy evaluation. Using 128,640 student trajectories (2.94 million events) with a 32-dimensional state representation and 12 pedagogical actions, the optimized policy achieved a +0.042-return improvement over a supervised next-item baseline under doubly robust estimation, with a bootstrap confidence width of ±0.021. Self-normalized estimators produced consistent rankings, reporting a +0.041 improvement with comparable uncertainty. Performance gains were horizon-stable and concentrated in medium horizons, where improvement increased from +0.012 at 1 step to +0.055 at 5 steps and remained positive through 10 steps. Safety analysis showed a shift toward bettersupported actions, increasing mean action support from 0.31 to 0.44 and reducing the low-support decision rate from 0.18 to 0.06. Uncertainty pruning activated on 11% of decisions, decreasing the high-uncertainty rate from 0.22 to 0.08 and reducing the maximum importance weight from 14.7 to 9.3, while effective sample size increased by 908. Student-level stratification indicated the strongest gains for mid mastery and mid engagement learners (mean improvement 0.046, median 0.044), with smaller but consistent benefits for high mastery learners driven by reduced repetition rather than correctness shifts. Ablation results confirmed that conservatism and pruning are complementary: removing conservatism increased tail risk and widened confidence intervals, while removing pruning increased evaluation variance despite similar mean return. These findings demonstrate that evidence-constrained offline reinforcement learning can produce deployable adaptive policies with measurable improvements and quantifiable safety guarantees under logged-data constraints.
Enterprise Knowledge-Grounded Human–AI Collaborative Feedback Generation Using Retrieval-Augmented Models Hanum Fatmah; M Itmamul Wafa
International Journal for Applied Information Management Vol. 6 No. 2 (2026): Regular Issue: July 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v6i2.122

Abstract

Adaptive feedback generated by large language models often suffers from limited auditability and inconsistent pedagogical intent, which constrains trust in real learning deployments. This study proposes an explainable adaptive feedback framework that combines retrieval-augmented generation with pedagogical rationale tracing, linking learner-state signals, retrieved evidence, and instructional move sequencing into a traceable rationale artifact. Across four conditions, the full model improves faithfulness to evidence from 0.66 to 0.88 and reduces scope violations from 7.4% to 1.9%. Learning proxies improve concurrently, with next-item accuracy increasing from 71.2% to 78.9% and revision uptake rising from 34.6% to 47.8%. Human rubric scores confirm higher instructional usefulness, where actionability increases from 3.6 to 4.3 on a 5-point scale while tone remains stable from 4.1 to 4.2. Ablation results show retrieval as the dominant driver of grounding, with faithfulness dropping to 0.62 and scope violations rising to 8.1% when retrieval is removed, whereas removing rationale tracing mainly degrades actionability from 4.3 to 3.7 and revision rate from 47.8% to 39.5%. Stratified analysis indicates the strongest benefits for low mastery and high frustration learners, where next-item accuracy improves from 61.0% to 70.0% and revision rate increases from 45.0% to 58.0%, alongside persistence gains from 68.3% to 79.5%. Mitigation controls further reduce evidence mismatch from 9.6% to 4.1% and rationale incoherence from 6.8% to 2.9%. The findings indicate that grounded generation and explicit pedagogical rationale tracing jointly improve effectiveness, accountability, and deployment readiness of adaptive feedback systems.
Continual Learning for Human–AI Collaborative Learning Analytics under Behavioral Drift Amaleswari Rajulapati; Sridevi V; S. Rajendra Prasad
International Journal for Applied Information Management Vol. 6 No. 2 (2026): Regular Issue: July 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v6i2.123

Abstract

Semester-to-semester non-stationarity undermines the reliability of adaptive learning analytics, particularly when predictive models are deployed without explicit drift monitoring and controlled updating. This study analyzes a 14-semester longitudinal panel constructed from learning management system traces and assessment records, covering 18–21 distinct courses per semester and 812–936 active students per term. Drift is concentrated in performance-relevant behavioral channels, with the strongest intensity observed in practice attempts, submission timeliness, and session regularity, alongside a pronounced regime shift around the mid-sequence semester. Under semester-forward evaluation, a static model yields mean macro-F1 of 0.706 with a worst-semester macro-F1 of 0.652 and high volatility across semesters (std 0.030). Periodic retraining improves mean macro-F1 to 0.724 and worst-semester macro-F1 to 0.681 (std 0.022) but remains sensitive to update timing. Drift-aware continual learning achieves the highest and most stable performance, improving mean macro-F1 to 0.742 and worst-semester macro-F1 to 0.711 while reducing temporal variance (std 0.015) and increasing mean AUROC to 0.812. Reliability gains are reflected in lower expected calibration error (ECE 0.039 versus 0.056 for static) and improved decision quality at fixed intervention capacity, raising risk precision from 0.62 to 0.69 and risk recall from 0.48 to 0.56 while reducing alert volatility (CV 0.14 versus 0.29). Fairness robustness improves under drift-aware updating, reducing mean subgroup recall gap from 0.118 to 0.082 and lowering the maximum recall gap from 0.172 to 0.121. Ablation shows that intermediate drift thresholds balance robustness and governance load, sustaining worst-semester performance with approximately 1–2 updates per semester and diminishing returns beyond moderate replay memory.
Measuring the Impact of Human–AI Collaborative Personalized Interventions through Temporal Causal Inference Ahmed Bahurmuz
International Journal for Applied Information Management Vol. 6 No. 2 (2026): Regular Issue: July 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v6i2.124

Abstract

Adaptive learning platforms frequently report performance improvements, yet many evaluations remain vulnerable to time-varying confounding because interventions are triggered by evolving learner states. This study evaluates three intervention families, adaptive sequencing, targeted hints, and remediation triggers, using a longitudinal causal framework with horizon-locked outcomes and learner-level cross-fitting. The analytic cohort includes 2,480 learners and 118,640 decision points observed across 12 instructional weeks, with median 41 decisions per learner. Intervention exposure rates per 100 decisions are 38.6 for sequencing, 24.1 for hints, and 8.9 for remediation, with higher targeting intensity in low-mastery strata. Causal estimates show distinct temporal signatures by intervention mechanism. Targeted hints yield the largest same-session improvement, increasing mastery by 2.4 points, but effects attenuate at 7 days (1.3 points) and 14 days (0.9 points). Adaptive sequencing provides more stable medium-horizon benefits, improving mastery by 1.6 points same-session, 2.8 points at 7 days, and 2.2 points at 14 days. Remediation triggers demonstrate delayed consolidation, increasing mastery by 1.1 points same-session, 3.4 points at 7 days, and 4.1 points at 14 days, albeit with wider uncertainty consistent with lower overlap and late-course concentration. Heterogeneity analyses at the 7-day horizon indicate sequencing peaks for mid-mastery learners, reaching 3.9 points under high engagement versus 3.4 under low engagement, while hints are most effective for low mastery with low engagement (1.6 points) and decline sharply for high mastery with high engagement (0.4 points). Remediation remains meaningful across strata, reaching 3.6 points for mid mastery with high engagement and 2.3 points for high mastery with high engagement, supporting a diagnostic targeting interpretation rather than uniform escalation. Robustness and diagnostic checks support internal validity. After weighting, standardized mean differences for key confounders fall to 0.05–0.09, and placebo effects on pre-decision outcome change remain near zero in magnitude (absolute value ≤0.05) across all intervention types. Overlap trimming of the lowest 5% support preserves the ranking of interventions, with only modest attenuation for remediation, and effective sample size remains adequate for sequencing and hints while declining for remediation in late decision indices. These findings justify a tiered deployment strategy where sequencing is the default optimization lever, hints are constrained to high-instability episodes and paired with post-hint practice allocation, and remediation is gated by high-confidence misconception signals with overlap and effective-sample-size monitoring.
Human–AI Collaborative Retrieval-Augmented Decision Intelligence for Enterprise Knowledge Bases in Financial Services Riswan Efendi Tarigan; Kevin Ariel Zen
International Journal for Applied Information Management Vol. 6 No. 2 (2026): Regular Issue: July 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v6i2.125

Abstract

Financial service institutions increasingly require knowledge systems that can retrieve policy-relevant information, synthesize organizational evidence, and deliver explainable decision support under strict governance constraints. This study proposes a Retrieval-Augmented Decision Intelligence framework for enterprise knowledge bases in financial services by integrating semantic retrieval, metadata-aware re-ranking, grounded augmentation, and explainable response generation into a unified managerial intelligence pipeline. The framework was evaluated using enterprise-style query scenarios covering compliance clarification, product guidance, risk review support, and service resolution. The results showed that the proposed framework outperformed a baseline generative model across all major dimensions, achieving Precision@5 of 0.86 compared with 0.71, NDCG of 0.88 compared with 0.74, grounding score of 0.84 compared with 0.68, usefulness score of 0.87 compared with 0.70, and an overall effectiveness index of 0.86 compared with 0.71. Category-level analysis indicated that hybrid re-ranking improved ranking effectiveness in all query types, with NDCG increasing from 0.84 to 0.89 for compliance queries, from 0.87 to 0.91 for product guidance, from 0.82 to 0.87 for risk review support, and from 0.79 to 0.85 for service resolution. Grounding performance remained strong across categories, reaching 0.90 for compliance clarification, 0.88 for product guidance, 0.82 for risk review, and 0.79 for service resolution, demonstrating that retrieved enterprise evidence substantially constrained unsupported generation. Expert evaluation further confirmed high managerial value, with average scores of 4.4 for clarity, 4.5 for actionability, 4.3 for trustworthiness, 4.4 for interpretability, and 4.5 for decision value on a five-point scale. Failure analysis identified outdated policy retrieval, cross-document ambiguity, terminology mismatch, insufficient escalation signaling, and partial evidence coverage as the main residual weaknesses, with outdated policy retrieval accounting for 18 observed cases and cross-document ambiguity for 14. These findings indicate that retrieval-augmented architectures can move beyond information access and function as decision intelligence systems that support traceable, evidence-grounded, and operationally meaningful knowledge work in regulated financial environments.

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