The rapid advancement of the industrial revolution and digital transformation has reshaped organizational systems, particularly in Human Resource Management (HRM), by introducing artificial intelligence (AI) as a tool for efficiency, effectiveness, and innovation. However, the challenge lies in ensuring that AI-based HRM practices align with sustainable development goals (SDGs). This study aims to explore how Digital Business graduates and undergraduate students, representing Generation Z as the future workforce, perceive the role of AI in HRM and its contribution to achieving specific SDGs, namely: SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 11 (Sustainable Cities and Communities). Using an exploratory qualitative approach, two focus group discussions were conducted with 40 participants consisting of students and graduates who have completed internships or worked in industries applying AI in HRM. The findings highlight that participants perceive AI as enhancing recruitment, training, performance evaluation, and data-driven decision-making. However, challenges such as data security, digital literacy gaps, and organizational culture resistance remain significant. The contribution of this study lies in emphasizing the perspective of Gen Z, who not only experience but also drive digital transformation in HRM. Their insights reveal the potential of AI to accelerate SDG achievements while underscoring the need for competency development in technology literacy and data analysis. Practical implications are directed to higher education institutions for curriculum adjustments and to organizations for fostering adaptive, innovative, and sustainable HR practices in the digital era.

This study aims to explore the impact of Big Data Analytics (BDA) on Customer Happiness (CH) in Marketing 4.0 (M4.0) Era in the Saudi healthcare sector. The purpose of the study is to examine how the integration of data-driven decision making and modern marketing strategies can enhance patient happiness. The sample consisted of 450 employees from various levels within healthcare organizations across Saudi Arabia. A quantitative research approach was used, using a structured survey to collect data on perceptions of BDA and M4.0 and their impact on CH. Statistical analyses were conducted to test the proposed hypotheses. The results indicate that both BDA and M4.0 have a statistically significant positive impact on customer happiness, with BDA enhancing personalized healthcare services and M4.0 improving patient happiness. Based on these findings, healthcare organizations are encouraged to invest in Big Data analytics tools and adopt Marketing 4.0 strategies, such as personalized marketing and digital patient engagement, to enhance patient experiences and happiness. It is also recommended that future studies explore patient happiness through big data analytics, and to expand understanding of these technologies in diverse healthcare settings.

Sensor data-driven decision support system for real-time optimization and impact assessment in concrete construction

In the age of digital revolution, organizations increasingly use artificial intelligence (AI) and machine learning (ML) to improve their data-driven decision-making, especially in human resource management. This paper makes a comparative evaluation of AI-driven data warehousing systems and ML methods for forecasting employee turnover and maximizing employee performance. The study compares top data warehousing platforms like Redshift, BigQuery, Snowflake, and Databricks and their coupling with ML models with regard to prominent workforce features. Qualitative findings from HR managers were also examined, in order to evaluate the real-world effect of these technologies on the productivity of the workforce and employment strategies. Research shows that AI-based data warehousing integrated with competent machine learning models drastically enhances attrition prediction accuracy, performance tracking, and strategic workforce planning. This research identifies the strategic advantages of combining AI-driven data warehousing with HR analytics, offering organizations actionable findings to choose the best AI-enabled solutions. The findings contribute to extending knowledge on efficient data strategies in lessening attrition as well as improving employee performance, aiding organizations in their pursuit of strategic human capital objectives.

The industrial sector is presently experiencing a significant transformation characterized by the integration of intelligent automation fueled by advancements in Artificial Intelligence (AI). This combination significantly boosts operational efficiency and facilitates data-driven decision-making. Such improvements allow for optimal resource distribution and enhance the accuracy of production planning. This paper intends to present the latest trends and ongoing innovations within the AI domain as it pertains to the manufacturing sector. Additionally, the review examines critical applications of AI in manufacturing, including predictive maintenance, quality assurance, process optimization, supply chain management, robotics and automation, as well as intelligent decision support systems. It also addresses the challenges faced by the manufacturing industry while exploring how AI can help alleviate these issues. Moreover, this study provides an in-depth analysis of recent developments in AI technologies such as explainable AI, collaboration between humans and robots, edge computing, and integration with the Internet of Things (IoT). The review concludes with recommendations that underscore best practices and identify potential collaborative opportunities.

ABSTRACT Tidal flats, which serve as a crucial transitional zone from land to sea, provides valuable habitats, potential land resources, and buffers against marine hazards. However, accurately and efficiently mapping high-frequency tidal flat topography poses challenges only relying on single-source data, which provides limited available imagery. In this study, we explored the integration of free-access multi-source optical imagery and ICESat-2 laser lidar data to derive annual tidal flat topography. Specifically, we established the relationship between inundation frequency and elevations based on two regression models: the simple linear model (SLM) and the third-order polynomial model (TPM). We found that the SLM model outperformed the TPM, which tended to overestimate elevations in areas with lower elevations. The proposed method was applied to China’s largest tidal flat (along Jiangsu central coast) with complex hydrodynamic conditions, achieving a root mean square error of 0.35 m compared to high-accuracy airborne lidar data. Based on the calibrated SLM model, we generated tidal flat topography covering 1749.89 km2 from a satellite-derived inundation frequency map, with elevations ranging from – 0.81–2.96 m (elevation difference: 3.77 m). This satellite-based method advances our understanding of coastal dynamics, and support data-driven decision-making for sustainable coastal development.

Enhancing students' academic performance in higher education is a primary goal, necessitating a systematic and adaptable approach. Traditional methods for analyzing student performance data often struggle with complexity and unpredictability, making them inadequate for handling intricate educational patterns. As a result, the development of fuzzy logic-based decision-making systems has become increasingly important<b>. </b>This study aims to design an accurate fuzzy logic system for predicting first-year physics students' academic achievement at Dire Dawa University.<b> </b>The model leverages the fuzzy library, defining membership functions for participation in experiments and discussions (categorized as low, medium, and high). It then establishes rules to map participation levels to predicted grades (poor, average, good, and excellent). The analysis involved applying the fuzzy logic system to a dataset and validating the predictions against actual grades. The findings revealed that the model accurately predicted grades for medium participation (e.g., 50.00 for 50% participation) but tended to overestimate high participation levels (e.g., predicting 85.00 when the actual score was 67.51).. This research contributes to educational technology by providing a flexible predictive tool that can be expanded to other STEM disciplines, enhancing data-driven decision-making in academic settings. These results demonstrate the effectiveness of fuzzy logic in managing educational uncertainties, though refinements are needed to address overestimation and incorporate additional variables such as study habits and prior knowledge.

Project management continues to face a high level of failures due to budget overruns, schedule delays, and insufficient quality, which directly cause financial inefficiency. This study examines how generative artificial intelligence (AI) can improve both operational and financial performance in project management. The purpose is to assess the impact of AI on efficiency with a focus on financial outcomes, evaluated through cost control, deadline compliance, and quality of deliverables.The methodology combines a literature review, scenario modeling across IT, construction, consulting, education, and research projects, and a comparative assessment using fuzzy logic to address uncertainty. In addition to operational aspects, financial indicators such as ROI, cost variance, and frequency of budget overruns were analyzed.The results show that generative AI improves efficiency across all life cycle phases. AI can reduce execution time by 10–15%, decrease budget deviations by 10–20%, and enhance quality through automation, advanced forecasting, and optimized resource allocation. Financial effects are most visible in knowledge-intensive projects (IT, consulting), where AI supports data-driven decision-making, accurate financial planning, and higher ROI. In construction, improvements are moderate, mainly through risk mitigation and planning accuracy.Findings confirm that AI does not replace classical methodologies (Agile, Waterfall) but strengthens them by improving financial discipline and compensating for weaknesses. For effective adoption, organizations must consider industry specifics, invest in staff training, and ensure reliable data and risk management. Generative AI emerges as a strategic driver of financial efficiency and competitive advantage in project management.

Purpose This study aims to assess and enhance the production efficiency and reliability of fertilizer manufacturing plants through the application of Markovian analysis. By integrating stochastic modeling with performance management theory, the research provides a predictive performance measurement framework that links reliability metrics to key business outcomes. The research develops a probabilistic framework to model system state transitions, evaluate system availability and optimize maintenance strategies to reduce downtime and improve overall plant performance. Design/methodology/approach A discrete-time Markov chain model is constructed to represent the operational dynamics of critical units within a fertilizer plant. Historical operational and maintenance data are analyzed to develop transition probability matrices that capture the likelihood of state transitions between operational, idle and failure conditions. Key performance metrics such as steady-state probabilities, mean first passage times and recurrence times are computed to assess long-term system behavior. Statistical hypothesis testing – including paired t-tests, Wilcoxon signed-rank tests and chi-square tests – is employed to validate improvements in reliability and efficiency following the implementation of Markov-based maintenance strategies. Regression analysis is also conducted to examine the relationships between operational parameters (e.g. downtime and failure frequency) and production output. Findings The analysis reveals that the production units remain in the operational (RUN) state approximately 61.01% of the time, compared to 53% prior to optimization. Markovian-based maintenance strategies significantly reduced average weekly downtime from 42 h to 29 h (p < 0.001). Weekly production output increased from an average of 1,250 tons to 1,375 tons (p < 0.001). A chi-square test confirmed statistically significant changes in system state transitions (p < 0.001), favoring increased operational continuity. Confidence intervals constructed for key reliability parameters further strengthened the robustness of the findings. Practical implications This study provides a data-driven methodology for improving maintenance planning and production reliability in fertilizer plants. By modeling system behavior through Markovian analysis and applying statistical validation techniques, maintenance managers can develop predictive strategies that reduce unplanned downtime and enhance production efficiency. The methodology is adaptable to other continuous process industries where uptime and reliability are operational priorities and can be integrated into existing performance management systems to support data-driven decision-making and strategic alignment of maintenance activities with productivity goals. Originality/value This study offers a novel academic contribution by applying discrete-time Markov chain modeling to fertilizer manufacturing using empirical operational data. It advances performance management research by integrating stochastic modeling with statistical validation to quantify production efficiency and system reliability. The linkage between probabilistic reliability metrics (e.g. steady-state probabilities and mean first passage times) and business key performance indicators (e.g. downtime and weekly output) provides a new data-driven framework for industrial performance evaluation. This work bridges theoretical modeling with applied maintenance strategies, offering a transferable methodology relevant to researchers and practitioners seeking to optimize reliability and productivity in continuous-process industries.

Abstract - This Crop Prediction and Production System that leverages Machine Learning to provide data-driven agricultural decision support tailored for Indian farming communities. The system employs a Random Forest classifier, achieving 87.5% accuracy by analyzing seven critical parameters: soil nutrients (N, P, K), temperature, humidity, pH, and rainfall to recommend optimal crops with confidence scoring. A key innovation is its multi-language interface supporting five Indian languages (English, Hindi, Kannada, Telugu, Tamil), effectively bridging the digital divide for non-English speaking farmers. The platform uniquely integrates predictive analytics with a digital marketplace for real-time price intelligence, land analysis tools for soil health and irrigation planning, and region-specific advisory services. Designed for accessibility, it features QR code-based mobile access, responsive design for low-bandwidth environments, and batch processing capabilities. Implemented using Flask framework with Bootstrap frontend, the system demonstrates practical deployment of ML in agriculture, enhancing productivity through scientific crop selection while maintaining cultural and technological relevance for diverse user groups. Testing with 50 farmers showed 82% satisfaction with predictions and strong preference for regional language interfaces. Key Words: Machine Learning, Agricultural Technology, Crop Prediction, Random Forest, Multi-language Interface, Precision Agriculture, Digital Marketplace, Soil Analysis, Mobile Accessibility, Indian Farming, Flask Framework, Decision Support System, Agricultural Informatics, Sustainable Farming, QR Code Integration.

The growth of online learning in higher education, particularly after the COVID-19 pandemic, has fostered the advancement of learning analytics, which nowadays relies greatly on capturing and mining data derived from systems such as Blackboard and Moodle. However, it remains difficult to identify all the variables having a direct bearing on academic success, and drawing advice from machine learning models trained to support data-driven decision making is challenging. Therefore, we have endeavoured to pair a descriptive model, which characterises the profiles of computer science students, with a predictive model, which relies on Bayesian networks to forecast academic success. To achieve this, we have looked for the factors directly influencing the academic performance of computing science students, and the common patterns of behaviour which characterise higher education students individually and as part of a cohort. Our approach has been tested with data provided by a Chilean institution—University of Bío-Bío. We have enhanced and supplemented the data employed in our investigation by means of two surveys distributed among all the different cohorts of the student population. Our predictive model can determine student outcomes with an accuracy rate above 97%.

With the deep development of the digital economy, big data and artificial intelligence technologies are reshaping enterprise business decision-making models. This paper systematically analyzes the core value of these two technologies in the field of business decision-making and explores their application paths in key scenarios such as customer relationship management, supply chain optimization, and financial analysis. The research finds that intelligent data processing provides enterprises with panoramic business insights, and machine learning algorithms significantly improve the accuracy and timeliness of complex decisions. Data-driven decision-making has achieved remarkable results in reducing operational costs, improving customer satisfaction, and optimizing resource allocation. Meanwhile, key challenges such as data security, technology costs, and talent development have been identified. Based on technology development trends and practical needs, this paper proposes strategic paths for building intelligent decision-making ecosystems, providing guidance for enterprise digital transformation.

This paper addresses the challenges and key principles of designing domain-specific datasets that can be used especially for automation of unmanned aerial vehicles. Such datasets play a key role in building intelligent systems that enable autonomous operation and support data-driven decisions. The study presents approaches we used for data collection, analysis and annotation, highlighting their importance and practical impact on real-world application. The preparation of a domain-specific dataset for automating unmanned aerial vehicles operations (such as navigation and environmental monitoring) is a challenging task due to frequently low image resolution, complex weather conditions, a wide range of object scales, background noise and heterogeneous terrain landscapes. Existing open datasets typically cover only a limited variety of unmanned aerial vehicles use cases, which restricts the ability of deep learning models to perform adequately under non-standard or unpredictable conditions. The object of the study is video data acquired by unmanned aerial vehicles for creating domain-specific datasets that enable machine learning models to perform autonomous object recognition, navigation, obstacle avoidance and interaction with an environment with minimal operator involvement. The subject focuses on the collection, preparation and annotation of video data acquired by unmanned aerial vehicles. The purpose of the study is to develop and systematize workflow for creating specialized datasets to train robust models capable of autonomously recognizing objects in real-time video captured by unmanned aerial vehicles. To achieve this goal, a workflow was designed for collecting and annotating video data, raw video data were acquired from unmanned aerial vehicles sensors and manually annotated using the Computer Vision Annotation Tool. As a result of this work, we developed a domain-specific dataset (UAeroNet) using an open-source annotation tool for object tracking task in real scenarios. UAeroNet consists of 456 annotated tracks and a total of 131 525 labeled instances that belong to 13 distinct classes.

This study examines the effects of the Data-Driven Decision-Making Teacher Training Program (DDDM-TTP), designed to enhance the quality of classroom teachers’ decisions regarding students at risk during the pre-referral process. The research employed a single-group pretest–posttest design. Before and after the implementation of the teacher training program, individual interviews were conducted with the participating teachers. In addition to these interviews, classroom observations were carried out, and teachers were asked to complete the educational assessment request form. The data were analyzed using content analysis, one of the qualitative analysis methods. The findings revealed meaningful changes in the way teachers conducted the pre-referral process. Prior to the training, teachers’ decisions were largely based on intuition and limited observations; however, after the training, teachers began to collect data in a more systematic, planned, and multidimensional manner. They regularly documented indicators related to attention, engagement, performance, and learning processes, and justified their decisions more reliably. Analysis of the educational assessment forms showed that teachers provided more detailed, observation-based explanations in terms of content, clarity, and alignment with student needs. Moreover, one teacher reconsidered the decision to refer a student to the Guidance and Research Center (GRC) and instead concluded that classroom-based interventions were sufficient after the training. Social validity findings indicated that both classroom teachers and school counselors found the program applicable, informative, and supportive of their professional development. In conclusion, DDDM-TTP contributed to teachers’ ability to conduct the pre-referral process more consciously, data-based, and professionally, thereby significantly improving the quality of their evaluation and decision-making practices.

In recent times, the rapid growth in construction waste generation has raised significant environmental and economic concerns in recent times. However, Building Information Modelling (BIM) has emerged as a promising solution for managing construction waste and promoting sustainability. BIM offers advanced capabilities for visualization, simulation, and data-driven decision-making, making it a valuable tool for optimizing waste reduction strategies in construction projects. This paper offers a thorough and in-depth review that examines the present and prospective trends of BIM research and its implications in the realm of construction waste management (CWM). Through a Bibliometric analysis, a total of 637 publications were collected from the "Web of Science" core database. Employing VOSviewer for analysis and visualization, co-occurrence, co-word analysis, cluster analysis, and co-citation analyses were conducted to explore influential authors and journals, high-frequency keywords, recent research trends, and potential future research directions in the field. The findings shed light on crucial topics in BIM for CWM, such as circular economy, recycling, waste estimation, and waste reduction. The study systematically analyzes and categorizes the existing literature, mapping the knowledge landscape, and highlights the main future trends in academic research on the integration of BIM and CWM. Looking ahead, future research is anticipated to focus on integrating BIM with Internet of Things (IoT) models, incorporating circular economy BIM systems, exploring green building using BIM models, implementing BIM-based design for deconstruction, and adopting multi-dimensional BIM frameworks. This comprehensive review provides innovative insights into the unique contributions of BIM for CWM, differentiating it from prior research and enhancing the paper's scholarly impact.

Purpose This study aims to examine the conceptual transformations occurring in educational administration as a result of digital transformation. It specifically seeks to explore the lived experiences of school administrators regarding the evolution of their digital leadership competencies and the integration of digital technologies into core management processes. The research focuses on how fundamental administrative concepts such as leadership, decision-making, communication and supervision are being redefined in the digital age. Design/methodology/approach A qualitative research paradigm employing a phenomenological design was adopted to capture the in-depth experiences of school administrators. Data were collected through semi-structured interviews with eight school administrators who were selected using criterion sampling based on their digital literacy, participation in digital training and postgraduate qualifications. The collected data were analyzed using thematic content analysis guided by Creswell's Data Analysis Spiral framework. Findings The findings reveal significant conceptual and practical shifts in educational administration, categorized under three main themes: a transition to data-driven decision-making, the establishment of systematic digital data management and the adoption of multichannel communication practices. Furthermore, administrators' digital leadership was characterized by key competencies such as mentorship, proactive technological integration and fostering an innovative school culture. These competencies were found to directly contribute to enhanced institutional efficiency and organizational agility. Practical implications The study provides actionable insights for educational policymakers, curriculum developers and school leaders. It underscores the need for targeted professional development programs focused on strategic digital leadership rather than mere technical proficiency. It also suggests that postgraduate programs in educational administration should revise their curricula to include courses on digital transformation, data-informed leadership and digital terminology to better prepare future leaders for the complexities of the digital era. Originality/value This research contributes to the educational administration literature by providing an empirical application of Sheninger's Digital Leadership Model and Rogers' Diffusion of Innovations Theory within a phenomenological framework. By explicitly identifying and analyzing the conceptual redefinitions of core administrative terms, the study moves beyond an instrumental view of technology, offering a nuanced understanding of digital transformation as a profound strategic and cultural paradigm shift in educational governance.

Educational planning is undergoing a profound transformation as digital technologies reshape policy design, institutional governance, and learning ecosystems. This systematic literature review aims to synthesize current scholarly evidence on how digital transformation influences educational planning processes and outcomes. Using a qualitative meta-synthesis approach, fifteen peer-reviewed international articles published between 2019 and 2025 were analyzed thematically. The findings reveal four dominant dimensions shaping educational planning in the digital age: data-driven decision-making and governance, technology integration in pedagogy, infrastructure and equity challenges, and digital leadership and capacity building. The review demonstrates that digitalization enhances planning precision, responsiveness, and inclusivity through real-time data analytics, flexible learning models, and cross-sector collaboration. However, persistent challenges remain, including digital inequality between regions, ethical concerns related to data use, and limited teacher preparedness. The novelty of this review lies in its integrative synthesis of fragmented empirical findings into a coherent conceptual framework that connects digital governance, pedagogical innovation, and human capacity development. Practically, the findings offer strategic insights for policymakers, school leaders, and educators in designing adaptive, ethical, and inclusive educational planning models. Overall, this review provides a synthesized conceptual framework and identifies critical directions for future research on sustainable and human-centered educational planning in the digital era.

The strategic alignment of smart city investments with public governance priorities has become a critical issue in the digital transformation of urban management, especially in developing and non-Western contexts. This study develops a hybrid Analytic Hierarchy Process (AHP)–Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to evaluate smart city initiatives in Konya Metropolitan Municipality in Türkiye. By integrating semi-structured interviews with decision-makers and a multi-criteria decision-making (MCDM) framework, we assess eight smart city dimensions—ranging from Smart People to Smart Environment—across five criteria: technical adequacy, cost-efficiency, integration, sustainability, and citizen impact. The findings reveal a strong prioritization of human capital and economic development, while environmental and infrastructural dimensions remain underemphasized. The analysis highlights persistent gaps in artificial intelligence (AI) adoption, interdepartmental data governance, and participatory citizen engagement, limiting governance maturity and long-term sustainability. Policy recommendations include embedding AI-supported decision-making, institutionalizing open data and interoperability standards, aligning investments with Sustainable Development Goals (SDGs), and designing inclusive cocreation platforms to enhance citizen-centric innovation.

Can technology become a strategic partner in human resource management? Traditionally, the HR function was perceived primarily as an administrative process relying on manual operations. However, the digital era, as in many other domains, has transformed the nature of HR management. The growing pace of global change has compelled organizations to adapt to technological innovation. The new era has fundamentally reshaped organizational approaches to work processes, creating an environment in which digital tools play a central role. Technological development has brought forth the need for process optimization. Consequently, HR management practices have gradually shifted from paper-based administrative operations toward a digital ecosystem. The introduction of Human Resource Information Systems (HRIS) has automated numerous HR functions, increasing efficiency and facilitating data-driven decision-making. Over time, the logical continuation of HRIS evolution became the integration of Artificial Intelligence (AI), which further enhanced and diversified HR processes. The synergy of these technologies has elevated HR management to a new analytical and data-driven dimension. With the growing economic and organizational influence of AI, its integration has become a transformative force across industries. The primary aim of this article is to explore the strategic significance of automation and artificial intelligence in human resource management. It examines the development of HRIS systems, their functional capabilities, and algorithmic integrations that ensure operational efficiency and data-based decision-making. Additionally, the paper presents international case studies illustrating the practical applications of HRIS and AI technologies in global organizations. Keywords: Artificial Intelligence (AI), Human Resource Information System (HRIS), automation

Financial institutions increasingly rely on data-driven decision systems; however, many operational models remain purely predictive, failing to account for confounding biases inherent in observational data. In credit settings characterized by selective treatment assignment, this limitation can lead to erroneous policy assessments and the accumulation of “methodological debt”. To address this issue, we propose an “Estimate → Predict & Evaluate” framework that integrates Double Machine Learning (DML) with practical MLOps strategies. The framework first employs DML to mitigate selection bias and estimate unbiased Conditional Average Treatment Effects (CATEs), which are then distilled into a lightweight Target Model for real-time decision-making. This architecture further supports Off-Policy Evaluation (OPE), creating a “Causal Sandbox” for simulating alternative policies without risky experimentation. We validated the framework using two real-world datasets: a low-confounding marketing dataset and a high-confounding credit risk dataset. While uplift-based segmentation successfully identified responsive customers in the marketing context, our DML-based approach proved indispensable in high-risk credit environments. It explicitly identified “Sleeping Dogs”—customers for whom intervention paradoxically increased delinquency risk—whereas conventional heuristic models failed to detect these adverse dynamics. The distilled model demonstrated superior stability and provided consistent inputs for OPE. These findings suggest that the proposed framework offers a systematic pathway for integrating causal inference into financial decision-making, supporting transparent, evidence-based, and sustainable policy design.