Data-driven decision making in human resources to optimize talent acquisition and retention

Service quality in the banking sector is a critical determinant of customer satisfaction, loyalty, and competitive advantage. As banks strive to meet the evolving expectations of customers and navigate an increasingly complex regulatory landscape, the role of data analytics in enhancing service quality has become paramount. This review explores how data analytics can be leveraged to improve service quality in the banking sector, offering insights into the methods, benefits, and practical applications of this approach. The review begins by outlining the importance of service quality in banking, emphasizing its impact on customer retention and the overall success of financial institutions. Traditional methods of assessing and improving service quality, such as customer surveys and manual audits, are often limited by their reactive nature and the inability to handle large volumes of data effectively. In contrast, data analytics provides a proactive and comprehensive approach, enabling banks to identify patterns, predict trends, and make data-driven decisions that enhance service delivery. Data analytics encompasses various techniques, including descriptive, predictive, and prescriptive analytics, each offering unique benefits for service quality improvement. Descriptive analytics allows banks to gain insights from historical data, identifying key areas for improvement. Predictive analytics uses statistical models and machine learning algorithms to forecast future customer behavior, enabling banks to anticipate needs and address potential issues before they escalate. Prescriptive analytics goes a step further by recommending specific actions to optimize service quality, based on the analysis of past and predicted data. Key areas where data analytics can significantly enhance service quality in banking include customer relationship management (CRM), operational efficiency, and risk management. In CRM, data analytics enables banks to personalize services, segment customers effectively, and predict their needs with greater accuracy. This personalized approach not only enhances customer satisfaction but also fosters loyalty and long-term relationships. Operational efficiency is another critical area where data analytics can drive improvements. By analyzing transaction data, banks can optimize processes, reduce waiting times, and improve the overall customer experience. For instance, data-driven insights can help banks streamline branch operations, optimize ATM placements, and manage workforce allocation more effectively. Risk management, particularly in the areas of fraud detection and credit risk assessment, also benefits from data analytics. Advanced analytics techniques can detect unusual patterns and flag potential fraud in real-time, reducing the risk of financial losses and enhancing trust. Similarly, predictive models can assess credit risk more accurately, ensuring that banks make informed lending decisions and maintain a healthy loan portfolio. The adoption of data analytics in banking is not without challenges. Issues such as data privacy, security, and the need for skilled personnel to interpret and act on data insights are significant considerations. However, with the right strategies and technologies in place, these challenges can be effectively managed, paving the way for substantial improvements in service quality. Data analytics offers a powerful toolset for banks aiming to enhance service quality. By leveraging data-driven insights, banks can deliver more personalized, efficient, and secure services, ultimately leading to greater customer satisfaction and competitive advantage. As the banking sector continues to evolve, the integration of data analytics into service quality improvement strategies will be essential for staying ahead in a competitive market.

This study investigates the application of machine learning techniques to predict employee turnover in high-stress sectors. The primary objective is to enhance retention strategies by accurately identifying potential turnover risks. The research utilizes a comprehensive dataset comprising various factors, including employee demographics, job satisfaction, performance metrics, and stress levels. Multiple machine learning algorithms, such as logistic regression, decision trees, random forests, and neural networks, are employed to build predictive models. The methodology involves data preprocessing, feature selection, model training, and evaluation. Cross-validation and hyper parameter tuning are performed to ensure the robustness and accuracy of the models. The performance of each algorithm is assessed using metrics such as accuracy, precision, recall, and the area under the receiver operating characteristic curve (AUC-ROC). Key findings reveal that machine learning models can effectively predict employee turnover, with random forests and neural networks demonstrating superior performance. Significant predictors of turnover include job satisfaction, stress levels, and performance ratings. The study concludes that integrating machine learning models into human resource practices can provide valuable insights for preemptive interventions, ultimately reducing turnover rates in high-stress environments. Future research should explore the integration of real-time data and the potential of deep learning techniques to further enhance predictive accuracy. Additionally, the ethical implications of using predictive models in HR decisions warrant careful consideration to ensure fairness and transparency. Keywords: Machine Learning (ML), Employee Turnover, Predictive Analytics, Human Resources (HR), High-Stress Sectors, Decision Trees, Random Forests, Extreme Gradient Boosting (XGBoost), Personalized Retention Strategies, Business Intelligence (BI) Tools, Data Quality, Ethical Considerations, Data Privacy, Natural Language Processing (NLP), Deep Learning, Real-time Data Analysis, Employee Engagement, Work-Life Balance, Organizational Performance, Data-Driven Insights.

Smart education and smart universities are based on active use of descriptive, diagnostic, predictive and prescriptive analytics as prescribed by the Gartner’s Data Analytics Ascendancy Model. This paper presents the up-to-date findings and outcomes of the research, design and development project at the InterLabs Research Institute at Bradley University (U.S.A.) aimed at application of a quantitative approach to student academic performance data analytics in general, and innovative Machine Learning (ML) models-based approaches and systems to predictive academic and learning analytics in particular. The goal of this research is to identify the best ML models in the Weka and Dataiku data processing systems based on various forms of student data representation and multiple evaluation criteria for quality of predictive analytics. The analyzed ML models included Support Vector Machine, Naïve Bayes, Random Forest, Random Tree, Linear Regression, Logistic Regression, k-Nearest Neighbors, Multilayer Perceptron, J48, and Decision Stump models. The evaluation criteria for predictive analytics included Correlation Coefficient, Mean Absolute Error, Mean Absolute Percentage Error, Root Mean Squared Error, Root Mean Squared Logarithmic Error, R2 Score for regression ML models and Correctly Classified Instances and Incorrectly Classified Instances for classification ML models. The obtained research outcomes provide a well-validated recommendation about what ML models should be used in student academic performance predictive analytics in smart education and smart universities.

HR practices for managing aging employees in organizations: the case of Thailand

PurposeEmployee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace. Embedded in this is the growing expectation of the human resource (HR) function to understand how workforce analytics informs the business and fuels success. This paper aims to explore how the HR function can achieve this.Design/methodology/approachThe evolution of the “Future of HR” and how it is moving from “descriptive and diagnostic” to “prescriptive and predictive.”FindingsAccording to KPMG’s 2019 Future of HR survey: 37 per cent of respondents feel “very confident” about HR’s actual ability to transform and move them forward via key capabilities such as analytics and artificial intelligence (AI). Over the next year or two, 60 per cent say they plan to invest in predictive analytics. Among those who have invested in AI to date, 88 per cent call the investment worthwhile, with analytics listed as a main priority (33 per cent). Despite data’s remarkable ability to deliver news insights and enhance decision-making, 20 per cent of HR believe analytics will be a primary HR initiative for them over the next one to two years, and only 12 per cent cite analytics as a top management concern.Research limitations/implicationsTaking a page from meeting customer needs, innovative technologies such as AI and the cloud, data analytics can give an organization the potential to gather infinitely greater amounts of information about customers.Practical implicationsToday’s workforce analytics focuses mostly on what happened and why. For instance, you might have tools for identifying areas of high turnover and diagnosing the reasons. But thanks to advancements in technology and data analytics capabilities, HR is better-positioned to be the predictive engine required for the organization’s success.Social implicationsThere has never been a better time for HR to create greater strategic value, as the potential for meaningful workforce insights and analytics comes within reach. Even advancements in cloud-based systems for human capital management are coming packaged with analytics and visualization capabilities, enabling HR leaders to integrate people data with other data sources, such as customer relationship management, for a full view of the business.Originality/valueThis paper will be of value to HR leaders and practitioners who wish to use predictive analytics and emerging technology to drive performance improvement and gain the insights about their workforces.

The chapter begins by illuminating the basic concepts of data analytics from human resources perspectives and its transformative impact on traditional human resources practices. It concentrates on the evolution of manual reporting to modern data-driven insight. the critical metrics for the success of HR and advanced analytics techniques and their potential to forecast future HR trends, mitigate risk, and streamline HR operations are examined. The chapter outlines the strategies for efficiently implementing data analytics in human resource practices by including robust human resource information system and data literacy among HR professionals. The chapter will conclude with a forward-looking approach toward implementing data analytics techniques in human resource practices and visualizing the role of data analytics in driving innovation, agility, and competitiveness in the workplace of tomorrow.

Operational data analytics is crucial in enhancing failure prediction and improving the availability of gas turbine power plants. However, existing research lacks a comprehensive approach that integrates real-time operational data with predictive maintenance models to address high failure rates and prolonged downtime. This study bridges this gap by investigating five GE MS5001 single-shaft, open-cycle gas turbine units, utilizing real-time operational data, historical maintenance records, and performance metrics. Predictive models, including Bayesian simulation and MATLAB-based analysis, were employed to assess failure probabilities and optimize maintenance planning. Key findings reveal a strong correlation between maintenance efficiency and turbine availability, with units exhibiting lower failure rates and shorter mean time to repair (MTTR) demonstrating higher reliability. Conversely, units with frequent failures and extended downtime underscore the limitations of traditional maintenance approaches. The study emphasizes the importance of implementing advanced predictive maintenance strategies to mitigate operational inefficiencies, prevent unexpected failures, and enhance turbine performance. By integrating data analytics with reliability engineering, this research presents a data-driven framework for enhancing the reliability of gas turbine plants. The study contributes to bridging the research gap by demonstrating how predictive analytics can transform maintenance strategies. The study recommends that future research should focus on refining predictive maintenance models through machine learning and AI-based analytics to further improve turbine efficiency and operational resilience. By integrating data analytics with reliability engineering, this study contributes to the advancement of maintenance practices in gas turbine power plants.

This study responds to the call of researchers, and is conducted in a non-western context in the country of Jordan. The study contributes to our understanding of human resource (HR) practices' impact on organisational effectiveness. The empirical analysis is based on theoretical prepositions that motivated employees through good HR practices stay longer and contribute positively to the overall financial performance of organisations. Rigorous statistical testing of the data on the population of financial firms shows that careful recruitment and selection, training and internal career opportunities have a positive impact on reducing employee turnover. Training, in particular, is found to have a strong positive impact on financial performance measured by return on assets and return on equity. Furthermore, the findings provide strong support for the direct approach in strategic HR management–performance research that a group of best HR practices will continuously and directly generate superior performance. Despite such compelling arguments, however, we did not find evidence to support the notion that a bundle of HR practices impact better on financial performance than individual HR practices. It is possible that the optimal configuration may not only be contingent on national context, but could be due to the sector and the specific characteristics of the firm.

PurposeThis paper aims to examine two types of age-related human resources (HR) practices, i.e. age-specific and age-inclusive HR practices and firm-level (meso-level) factors that foster or hinder the implementation of these two types of practices.Design/methodology/approachBased on a cross-case analysis of four firms across industries in Thailand, a developing country, the empirical evidence draws on semi-structured interviews with the top managers, HR managers and aging employees of four firms; field visits; nonparticipant observations; and a review of archival documents and Web-based reports and resources.FindingsThis paper proposes that age-specific HR practices primarily include those HR practices under the regulation HR bundle and some HR practices under the maintenance and recovery HR bundles. Additionally, the factors fostering the implementation of age-specific HR practices in firms include group corporate culture, nonunionism within the workplace, paternalistic leaders, a focus on the development of internal labor markets within firms and the need for tacit knowledge transfer from aging employees to younger-generation employees, whereas the factors hindering the implementation of age-specific HR practices in firms include age biases within firms. Moreover, age-inclusive HR practices primarily include HR practices under the development HR bundle and some HR practices under the maintenance and recovery HR bundles. Additionally, the factors fostering the implementation of age-inclusive HR practices in firms include the procedural justice climate, the transition from a family ownership structure to a professional ownership structure and result-/output-based corporate culture, whereas the factors hindering the implementation of age-inclusive HR practices in firms include experience-/seniority-based corporate culture. In fact, some of the meso-level factors that foster or hinder the implementation of age-specific and age-inclusive HR practices tend to be influenced by the national institutional and cultural contexts of the developing country where firms that implement such HR practices are located.Originality/valueThis paper aims to fill the research gap by examining both age-specific and age-inclusive HR practices. Additionally, this paper analyzes the factors fostering or hindering the implementation of these two dimensions of age-related HR practices across firms by using a case study of firms in Thailand, a developing country. To date, most studies in this area have focused on one of these dimensions, while comparisons between different HR dimensions are rather scarce. Finally, this paper contributes to the prior literature on strategic HR and comparative institutional perspective on HR strategies and practices as proposed by Batt and Banerjee (2012) and Batt and Hermans (2012) that future research should go beyond the meso-level (organizational) context. In this regard, some of the factors that foster or hinder the implementation of age-specific and age-inclusive HR practices tend to be influenced by the national institutional and cultural contexts of the developing country of Thailand.

This study investigates the impact of technological advancements on human resource (HR) practices within organizations in Saudi Arabia, aiming to understand how modern technologies are reshaping HR functions and strategies. Employing a mixed-methods approach, the research was conducted using two primary data collection techniques: an online survey and in-depth interviews. The online survey garnered responses from 200 HR professionals from various industries in Saudi Arabia, providing quantitative insights into the current trends, benefits, and challenges associated with the integration of technology in HR practices. Additionally, 50 semi-structured interviews were conducted with HR managers and executives to gain qualitative perspectives and deeper understanding of the strategic implications and experiences related to technology adoption in HR processes. The findings reveal significant transformations in recruitment, talent management, and employee engagement, driven by technologies such as AI and data analytics. However, the study also highlights challenges such as the need for skill development and change management to effectively leverage these technologies. This research offers valuable contributions to both academic literature and practical applications, suggesting strategies for organizations to optimize their HR practices in the era of digital transformation.

The dynamic landscape of technology compels organizations to re-evaluate Human Resource (HR) practices for optimal performance. This paper investigates how advancements like AI and HR analytics reshape core HR functions – recruitment, training, performance management, and employee engagement. We analyze how these transformations can lead to increased efficiency, a more skilled workforce, and a more engaged employee base. Furthermore, the paper explores how data-driven insights from HR technologies can be harnessed to optimize HR strategies for superior organizational performance. We acknowledge potential challenges of technological adoption, such as employee resistance and the need for upskilling. Finally, recommendations are proposed to effectively integrate technology into HR practices, maximizing the positive impact on employee well-being and overall organizational success.

This study examines the relationship between strategic human resource (HR) practices and financial performance, focusing on optimizing HR investments for sustainable economic success. The research addresses HR practices such as training, competency development, and reward systems in enhancing organizational outcomes. A systematic literature review (SLR) methodology synthesized findings from recent industry studies. This approach allowed for an integrative analysis of theoretical frameworks, including the Resource-Based View (RBV) and Agency Theory, to contextualize the impact of strategic HR practices on financial performance. The study highlights the significant influence of HR practices on organizational productivity, operational efficiency, and cost reduction. Practices like Green Human Resource Management (GHRM) enhance sustainability and improve reputation and financial outcomes. Integrating technology, such as data analytics and performance tracking systems, was identified as a crucial enabler for decision-making and resource optimization. The findings also emphasize aligning HR planning with organizational strategies to ensure coherent and effective workforce management. This research contributes to academic literature and practical applications by offering actionable strategies for optimizing HR investments. It provides a roadmap for managers to implement innovative HR practices that align with business objectives, foster sustainability, and enhance competitiveness. This study's limitations, including its reliance on secondary data, suggest avenues for future empirical research to validate and expand on these findings.

In the contemporary landscape of business, the fusion of data analytics and eco-innovation has emerged as a potent force for organizational advancement. This abstract presents a conceptual model that delineates the integration of data analytics into Human Resources (HR) practices for fostering eco-innovation, specifically tailored for the dynamic and creative realms of the fashion and arts sectors. The fashion and arts industries, characterized by rapid trends and creative dynamism, face increasing pressure to align their practices with sustainability imperatives. Concurrently, the utilization of data analytics in HR functions has gained prominence for its potential in optimizing decision-making processes. This conceptual model proposes a strategic framework that amalgamates these two domains, aiming to catalyze eco-innovation within organizations operating in the fashion and arts sectors. At its core, the model underscores the importance of leveraging data analytics to inform HR practices towards sustainability goals. By harnessing big data analytics, organizations can gain insights into various facets of their operations, ranging from supply chain management to talent acquisition and retention strategies. These insights serve as the foundation for devising HR interventions that prioritize eco-friendly practices, such as reducing carbon footprint, optimizing resource utilization, and promoting ethical labor practices. Furthermore, the model advocates for a holistic approach that integrates eco-innovation initiatives into the organizational culture. This entails fostering a mindset shift among employees, wherein sustainability becomes ingrained in the organizational ethos. Through targeted training programs, awareness campaigns, and incentive structures, employees are empowered to contribute actively to eco-innovation efforts. Moreover, the model emphasizes the significance of strategic partnerships and collaborations within the industry ecosystem. By collaborating with stakeholders across the value chain, organizations can amplify their impact and drive systemic change towards sustainable practices. The proposed conceptual model serves as a roadmap for fashion and arts organizations seeking to harness the power of data analytics to drive eco-innovation within their HR practices. By embracing this model, organizations can not only enhance their competitive advantage but also contribute positively to environmental preservation and societal well-being.
 Keywords: Data Analytics, Eco-Innovation, HR, Model, Fashion, Arts, Review.

Purpose– Prior research in the area of organizational change highlights the critical role played by HR practices during organizational change as it may require altering employee behavior to support the change direction. human resource (HR) function is considered to be well positioned to influence employee behavior by institutionalizing HR practices that support change. Further there is a significant body of literature that suggests that employee behavior is significantly influenced by the perceptions of HR practices during change. HR practices which create positive employee perceptions increase employee commitment to change. The purpose of this paper is to provide a conceptual framework that identifies critical HR practices that support organizational change and examines their impact on employee perception and commitment to change.Design/methodology/approach– First, an extensive literature review on organizational change at macro level has been done to identify critical practices desired from key organizational members during organizational change. Second, a case for importance of HR function as a key organizational member during change is presented. Further literature on effectiveness of HR practices adopted by HR professionals during organizational change is examined to find out the gap areas. Third, literature on employee perception and commitment to change is examined to find out possible linkages to HR practices during organizational change. Finally, eight propositions are presented to build an integrated conceptual framework identifying critical HR practices during organizational change and their impact on employee perception and commitment to change.Findings– The study suggests that HR practices undertaken in the area of culture, leadership, cross functional integration, training, communication and technology if introduced and implemented will positively influence employee perception reducing resistance and increasing commitment to change. Therefore assessing employee perception about critical HR practices at different stages of change initiation, implementation and consolidation can enable understanding about employee commitment to change. This would also help HR professionals understand how effective the HR practices implemented during change have been.Originality/value– This framework can be used by the researchers and practitioners to study, guide, frame and model empirical research into the area of studying critical HR practices during organizational change. So far literature provides a generic view of desired organizational practices during change. Moreover there are few studies available on employee perception about HR practices implemented during organizational change and its impact on employee commitment to change. The framework presented in this paper would help explore the effectiveness of specific HR practices implemented during change by evaluating its impact on employee perception and commitment to change.

Exploring human resource management intelligence practices using machine learning models