International Conference on Technological Intelligence and Business Strategies

Authors - Max Angelo D. Perin, Lenie B. Maligmat, Darrel A. Cardana, Renante S. Digamon, Joan Mae G. Lagumbay, Cecilia T. Gumanoy
Abstract - The Quality Assurance Office of a Philippine state university campus conducts 7S evaluations across all offices each semester, producing numeric scores and written evaluator comments. Consolidating the narrative comments has depended on manual review, which is time-consuming across more than a hundred offices per cycle. This paper describes a two-phase AI-assisted analytics pipeline. Phase 1 retrieves audit records from a MySQL database via a stored procedure, formats them with a Python ETL script, and submits them to Grok (xAI) to draft scorecards and action items; evaluators then review the drafts be-fore consolidation into the official PDF report. Phase 2 parses the validated PDF with Python to extract structured fields and compute descriptive statistics, office rankings, a priority index, and TF-IDF text clustering. Applied to the November 2025–January 2026 cycle (112 offices; 107 scored, 5 with no submission), most units cluster in the moderate-to-great compliance range while a meaningful minority fall below threshold. Among the top 25 priority offices, Standardize (20/25) and Safety (19/25) are the most frequently flagged dimensions. The pipe-line shows that AI assistance structured around human review can accelerate QA consolidation while preserving evaluator accountability.

Authors - Nur Fajrina, Felina C. Young, Rosita Widya Putri
Abstract - This study investigates the relationships among Stakeholder Integration (STI), Digital Transformation Capability (DTC), Absorptive Capacity (AEC), and Sustainable Supply Performance (SSP) within a knowledge-intensive supply chain context. Employing a quantitative methodology alongside Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from 262 respondents involved in strategic and operational functions. The results reveal that stakeholder integration significantly enhances digital transformation capability, thereby strengthening absorptive capacity. Both digital transformation capability and absorptive capacity have direct positive effects on sustainable sup-ply performance. However, stakeholder integration does not directly influence sustainable supply performance. Instead, its effect becomes significant only when mediated by absorptive capacity, indicating that internal knowledge assimilation and utilization mechanisms are essential for translating collaborative efforts into sustainability outcomes. The results highlight the critical role of dynamic capabilities in accomplishing sustainable supply performance, particularly in environments characterized by digital transformation and stakeholder complexity. The study contributes theoretically by integrating stakeholder theory and dynamic capability perspectives, emphasizing absorptive capacity as a key mediating mechanism. The results suggest that firms should complement external stakeholder collaboration with investments in digital infrastructure and organizational learning systems to enhance long-term sustainability performance.

Authors - Ayush Ghumare, Reena S. Satpute
Abstract - Mobile application development has evolved rapidly with the emergence of advanced technologies such as 5G connectivity, Artificial Intelligence (AI), Machine Learning (ML), and Mobile Edge Computing (MEC). These technologies are transforming the mobile ecosystem by enabling the development of intelligent, data-driven applications and accelerating development cycles. Mod-ern mobile applications are expected to provide real-time services, personalized user experiences, and seamless connectivity, which has significantly increased the complexity of mobile application design and implementation. It is resulting into many challenges. One of the major challenges in mobile application development is the inherent limitation of mobile devices, including restricted pro-cessing power, limited memory capacity, and battery constraints. Developers must optimize application performance while ensuring energy efficiency to pre-vent excessive battery consumption and degraded user experience. Additionally, the increasing reliance on third-party libraries and analytics tools may introduce security vulnerabilities, creating potential security gaps within applications. These risks are often intensified by the lack of specialized security expertise within development teams, raising concerns related to data privacy, application security, and software supply chain vulnerabilities. Another challenge is platform fragmentation, particularly within the Android ecosystem, where diverse devices, operating system versions, and hardware configurations complicate compatibility and performance optimization. This diversity increases testing complexity and development costs. Furthermore, integrating AI and ML models into mobile ap-plications requires careful decisions regarding cloud-based versus on-device pro-cessing. Therefore, developers must balance scalability, performance, security, and energy efficiency when designing modern mobile applications. This study presents systematic literature evaluation methodology, comparative analysis of native and cross-platform paradigms, software supply chain security frameworks, measurable energy optimization strategies, and practical industry case studies from healthcare, fintech, and mobile commerce sectors.

Authors - Murali Mohan Reddy Seelam, VyshnaviThanneeru, Ajay Kumar Reddy Vemireddy, Srilatha Kudumula
Abstract - This paper shows a new approach to implement the Agentforce-NS framework to provide zero touch salesforce deployment pipelines by integrating it with the Neuro Symbolic AI Agents. Even though the complete salesforce deployment pipelines have been automated end to end, it has been very difficult to achieve zero touch deployments due to its nature of the handling of metadata due to the interdependency of the components within the salesforce. The regular pipeline processes still heavily depend on the manual intervention to resolve the merge conflicts, resolve the dependency errors, working on the roll back deployments and following the compliances. The architecture we are proposing will solve all these problems by integrating the adaptive and predictive capabilities of the neural networks with rule based, transparent precision of the symbolic reasoning. The proposed Agentforce architecture will have five agents that will collaborate and will execute the deployments without any human intervention. These five agents are used to learn the deployment strategies, roll back planning, analyzing the metadata, autonomous execution and verification of the governance. After many tests in the enterprise level environments, we see that it is resolving so many blockers, issues and increasing the deployment success rate, improving the governance, and reducing the meantime to recover. By covering the technical gap between logical interface and the deep learning, the Agentforce-NS represents a break through advancement to have the fully automated, autonomous and auditable salesforce devops pipelines.

Authors - Shreya S. Partake, Reena S. Satpute
Abstract - Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, achieving human-level performance on many semantic and syntactic benchmarks. However, their competence in pragmatics—the study of how context shapes meaning—remains a critical and underexamined frontier. This paper presents a unified analysis of the “pragmatic gap” in LLMs, arguing that it stems from a fundamental distinction between the co-textual statistical patterns LLMs are trained on and the contextual world knowledge humans use for inference. We first establish a theoretical baseline by reviewing foundational linguistic concepts, including Grice’s maxims, implicature, presupposition, speech acts, and deixis. We then systematically evaluate LLM performance, contrasting successes in pattern-rich tasks like coreference resolution with systemic failures in tasks requiring novel inference, such as non-conventionalized indirect speech acts and irony. We analyze the development of new evaluation tools, particularly the Pragmatics Understanding Benchmark (PUB), which quantifies the persistent gap between model and human performance. Subsequently, we synthesize emerging technical solutions, including “thought-based” fine-tuning and the injection of Gricean principles into Retrieval-Augmented Generation (RAG) frameworks. Finally, we dissect the profound cognitive and philosophical implications of this gap, critically examining the debates on the Symbol Grounding Problem and Theory of Mind (ToM). We conclude that while LLMs can pass “literal” ToM tests, they fail “functional” ToM, revealing them to be sophisticated co-text manipulators rather than context-aware agents. We propose that future progress lies in developing a “machine pragmatics” based on probabilistic models rather than flawed anthropomorphic imitation.

Authors - Ayushi Chapate, Reena S. Satpute
Abstract - Natural language helps us to interact with the computer through human language. This article investigates how Natural Language Processing (NLP) can enhance our understanding of social media changes. To its audience, social media provides a large - arguably unlimited - and otherwise untapped linguistic re-source, revealing information about government behavior, civic participation, in-dividual mental well-being, and consumption behavior, among many other things. Using machine learning analytical methods such as sentiment analysis, topic modeling, stance detection, and misinformation tracking, researchers can begin to study the social, psychological, and economic implications of web-based inter-action. In terms of civic and political implications, to analyze user-generated con-tent, discourse networks, and hashtags using NLP applications can produced new insights into online mobilization and collective action. For example, researchers studying the political movement’s #MeToo and #BlackLivesMatter, based on analysis of Twitter data, have employed topic modeling techniques to reveal their influence and significance in innovative ways. From a psychological perspective, NLP methods make it possible to examine prevalent mental health indicators across separated populations, through the analysis of emotional tone, pronoun use, and distress markers. In studies conducted between 2020–2025, the application of BERT based embedding models were found to detect online indicators of depression, anxiety, and social comparison leverage's based on word meaning. Further, understanding the depth of these psychological consequences remains nebulous and limited to a range of social categories in the digital landscape, similar to previous notions of 'self-checking' across the digital commons exploring citizen engagement.

Authors - Jes Maries M. Mendez, Max Angelo D. Perin, Joan Mae G. Lagumbay, Mae S. Dagupan, Elizabeth A. Orapa, Marcelina S. Butlig
Abstract - Educational tours are widely used in higher education to connect class-room learning with real settings, yet evaluations often stop at overall ratings that do not explain why students endorse a tour or which delivery issues weaken the experience. This study applies a student experience intelligence workflow that integrates survey analytics with offline text mining to produce planning-relevant evidence. A survey of 156 students captured demographics, three 10-item Likert constructs—motivation, perceived effectiveness, and problems encountered (4-point scale)—a recommendation rating, and open-ended comments. Responses were cleaned through category standardization and rule-based numeric conversion. Internal consistency was good for motivation (α = 0.877) and excellent for effectiveness (α = 0.960) and problems (α = 0.958). Learning beyond classroom instruction (M = 3.71) and interest in tour inclusions (M = 3.68) led motivation; creative learning (M = 3.67), resourcefulness (M = 3.66), and social skills (M = 3.65) led effectiveness; tour expense (M = 3.21) and short time per attraction (M = 2.60) led problems. 73.1% gave the top recommendation. Recommendation correlated positively with motivation (ρ = 0.317, p < 0.001) and effectiveness (ρ = 0.328, p < 0.001); a binary logistic model showed perceived effectiveness as the strongest predictor of the top recommendation category. Open-ended comments (171 entries) were summarized through TF–IDF with K-Means clustering (k = 6) and complemented with a VADER polarity pass on 155 meaningful entries (68.4% positive, 21.9% neutral, 9.7% negative; mean compound = +0.365). The combined evidence points to improvements that preserve educational value while addressing cost and pacing, and shows that the workflow is portable to other programs and experiential learning activities.

Authors - Kent Cyryl A. Campit, Christian Kelvin Gonzales
Abstract - This study explored a data-driven approach to evaluating citizen feedback within the Provincial Government of La Union (PGLU) by integrating quantitative and qualitative analytical techniques. Traditional feedback systems in government offices often rely on averages and summary reports, limiting the ability to capture deeper citizen experiences and concerns. To address this gap, the research transformed paper-based feedback forms into a structured digital dataset covering responses from 34 frontline offices and service units from July 2025 to January 2026. The study applied Customer Satisfaction Score (CSAT), Weighted Mean, and Range of Interval to measure and classify service performance levels. For qualitative analysis, Latent Dirichlet Allocation (LDA) was used to identify recurring themes in open-ended responses, while a dual-model sentiment analysis approach combining VADER and RoBERTa classified citizen feedback into positive, neutral, and negative sentiments. The analytical pro-cesses were implemented using Microsoft Excel, Google Sheets, and Python through Google Colaboratory. Findings revealed consistently high satisfaction ratings across offices, while qualitative analysis uncovered recurring themes related to service efficiency, staff assistance, facility conditions, and operational concerns. RoBERTa demonstrated better contextual understanding and achieved higher performance metrics compared to VADER. The study further developed an Observed Satisfaction Classification Framework to support evidence-based decision-making and service improvement. Ultimately, the re-search demonstrated how citizen feedback can be transformed into actionable governance insights that promote transparency, accountability, and continuous improvement in public service delivery, aligned with Sustainable Development Goal 16.