JANOLI International Journal of Human Resource and Management
The JANOLI International Journal of Human Resource and Management (JIJHRM) is a peer-reviewed, open-access publication that publishes articles monthly. It serves as a platform for researchers, HR professionals, and business leaders to share innovative insights, strategies, and best practices in human resource management. As a globally recognized journal, it covers fundamental theories, modern HR techniques, and real-world applications across various industries. Additionally, the journal plans to feature special issues on emerging trends and challenges in workforce management and organizational development. Multidisciplinary Subjects: Business Management Public Relations SCOPE: Strategic Human Resource Management Talent Acquisition and Workforce Planning Employee Engagement and Organisational Behaviour Leadership and Change Management Performance Management and Productivity Diversity, Equity, and Inclusion in the Workplace HR Analytics and Data-Driven Decision Making Digital Transformation and HR Technology Training, Learning, and Development Labor Laws, Ethics, and Corporate Governance International HR Practices and Global Workforce Management
Shanti Educational Research Foundation, India
Leadership Dynamics in HR: Balancing Employee Well-being and Organizational Performance in Modern Enterprises
This paper discusses the dynamics of HR leadership and how organizations balance employee well-being with performance objectives. It investigates strategies for harmonizing these priorities, focusing on definitions of well-being, performance metrics, integration strategies, cultural influences, and technological impacts. The study, which employed qualitative methods such as in-depth interviews with HR leaders and case studies of innovative organizations, identified five key findings: comprehensive well-being metrics, balanced performance evaluation frameworks, integrated well-being and performance strategies, adaptive cultural leadership, and ethical technological integration. The study therefore highlights the significance of cultural adaptability and ethical practices while presenting a holistic framework for HR leadership. Future studies should be extended to various industries and methodologies to test these results.
Performance metrics Qualitative research Well-being frameworks Adaptive leadership
Strategies for Talent Acquisition and Retention: Addressing Challenges in a Competitive Job Market
This study explores the obstacles facing organizations in attracting and retaining talent in a competitive job marketplace. The study delves into the role played by employer branding, competitive compensation packages, company culture, professional development opportunities, and flexible work arrangements. Qualitative research methodology was conducted by gathering information through interviews and focus groups across industries. It will reveal the value of customized approaches, such as alignment of employer branding with organizational values, compensation package design, building cultural fit, investment in continuous development, and work models flexibility. The integrated approach to talent management, as concluded in the research, is highlighted for organizational success and actionable insight.
Company Culture Compensation Strategies Employer Branding Talent Acquisition
Analyzing Factors Affecting Human Productivity in Logging Machinery Operation
This paper investigates the factors affecting human productivity when operating logging machinery, with a specific focus on the impact of training machines and simulators. It evaluates how these tools influence the results of training and examines the psychophysiological traits that affect the precision of guiding the logging machine both on the horizontal plane and through boom extension. The study presents a novel approach for testing these traits using author-developed methods, which were applied to a group of cadets. The results of the tests are compared with those obtained from final examinations required to complete logging machinery operation training. The findings suggest that the author-developed testing methods provide an effective measure of operator precision and productivity in comparison with traditional evaluation methods. Furthermore, the research highlights the role of simulators in improving training outcomes by enhancing operator skills and reducing errors. The paper concludes that using advanced testing techniques can better assess the psychophysiological factors influencing logging machinery operation and contribute to more efficient training programs. This research will be of particular interest to professionals in human-machine interaction and logging machine training, providing valuable insights into optimizing training processes and improving operator performance in the forestry industry.
Copyright (c) 2025 JANOLI International Journal of Human Resource and Management
Impact of Atmospheric Pressure on Driver Physiology in Mountainous Roads of Kyrgyzstan
This study investigates the impact of atmospheric pressure on vehicle drivers in mountainous regions, focusing on the Bishkek-Naryn-Torugart international highway in Kyrgyzstan. The highway spans varying altitudes, with control points at Torugart pass (3752 m), At-Bashy (2046 m), and Kemin (1120 m). The study examines how altitude affects blood pressure in drivers, particularly in high-altitude conditions. Blood pressure measurements at these points showed a notable increase at higher elevations. At Torugart pass, 24% of drivers experienced elevated blood pressure readings (140-159/90-99 mmHg), compared to 19% at At-Bashy and 5% at Kemin. Moderate hypertension (160-179/100-109 mmHg) was observed in 7% of drivers at Torugart and 5% at At-Bashy, with none recorded at Kemin. The primary cause of the elevated blood pressure was identified as disruptions to the drivers' work-rest patterns due to the challenging conditions. Based on these findings, the study recommends implementing rest areas along the route and establishing guidelines for driver work schedules to reduce health risks. These measures aim to enhance safety for drivers navigating high-altitude roads in Kyrgyzstan.
Enhancing Fault Detection in Hybrid Electric Vehicles using Kernel Orthonormal Subspace Analysis
Hybrid electric vehicle (HEV) performance and safety are critical areas of focus in modern automobile technology, with fault detection being a key challenge. Traditional methods often fall short when it comes to detecting complex faults in the HEV powertrain system, as these faults exhibit nonlinear behaviors. This paper introduces a novel data-driven approach for fault detection in HEV powertrains using Kernel Orthogonal Subspace Analysis (KOSA). The KOSA method addresses the limitations of linear Orthogonal Subspace Analysis (OSA) by mapping nonlinear problems to a higher-dimensional space through a kernel function, thereby enabling more effective fault separation. This transformation, combined with the dimensionality reduction capabilities of OSA, allows KOSA to detect complex faults in the powertrain system more efficiently. Experimental results from both a nonlinear model and real-world data from the HEV demonstrate that KOSA outperforms both OSA and Kernel Principal Component Analysis (KPCA) in terms of fault detection accuracy and robustness.
Kernel Orthogonal Subspace Analysis (KOSA)
A Multicriteria Approach to Selecting Methods for Multispectral Earth Remote Sensing Data Analysis
This article presents a novel approach utilizing qualimetry methods for selecting models and polymodel complexes to automate the process of calculating Earth remote sensing (ERS) data, particularly in the context of analyzing complex natural and technical systems. The proposed methodology is applied to the task of selecting calculation methods for forest sustainability indicators. In scenarios where multiple alternative methods and models can be applied at each stage of data processing, the approach employs multicriteria comparative analysis based on a set of key indicators. These indicators include cost, efficiency (calculation duration), and accuracy (the quality of the calculation result). The solution algorithm is demonstrated through the selection of a method to assess the consequences of forest fires. The results are presented in a table, allowing users to assess the trade-offs between different methods based on partial indicators. This algorithmic approach facilitates the automation of the selection process, simplifying the application of complex ERS data processing methods for end-users. Additionally, the approach expands the potential for scaling ERS data results from smaller to larger forest areas, offering greater flexibility and applicability.
Exploring Jet Engine Performance Impact and Emission by Biodiesel: An Empirical Examination of Engine
This study investigates the impact of biodiesel on jet engine performance and emissions, specifically focusing on the engine. With increasing pressure to reduce aviation's environmental footprint, alternative fuels such as biodiesel are being considered for their potential to mitigate NOX and CO2 emissions. The study employs a quantitative methodology to analyse the effects of biodiesel blends on engine performance and emission levels. Key research questions include how biodiesel influences NOX emission levels, the relationship between biodiesel blend ratios and CO2 emissions, and whether biodiesel can maintain acceptable engine performance. Additionally, the study examines the quadratic behaviour of NOX emissions in response to increasing biodiesel concentrations and evaluates the feasibility of biodiesel as a sustainable aviation fuel. Using simulation data from 2023, the research identifies key relationships between biodiesel blend ratios, emissions, and performance, demonstrating that biodiesel can effectively reduce NOX emissions, although it results in a proportional increase in CO2 emissions. The study concludes by emphasizing the importance of optimizing biodiesel blends for aviation, advocating for future research to explore real-world applications and long-term performance outcomes.
The Future of 3D Mapping: Analyzing Two Popular Methods in Photogrammetry
In the past 15 years, mapping technology has become increasingly vital for the development of smart cities, with 3D maps gradually supplementing traditional 2D maps. These 3D maps are now widely utilized in cartography to provide a detailed, three-dimensional perspective of landscapes and buildings. This paper examines the concept of 3D maps and compares two prominent methods for their creation. In this study, one 3D map was generated using photogrammetric 3D stereo-restitution, while the other was created by automatically extruding a LiDAR point cloud with 2D vector polygons. Upon comparing the two methods, we found that their accuracy is comparable, with performance largely determined by the quality of the input data. We also observed that constructing a 3D map using photogrammetry requires significantly more time than the LiDAR-based approach. As 3D maps play an increasingly important role in mapping, the demand for more precise and comprehensive field data is growing. With the availability of better field data, a clearer determination of which method yields the most accurate 3D map could be made. The rapid evolution of 3D mapping technology, along with its growing applications in fields like surveying and material monitoring, is essential to the development of smart cities. Ultimately, the advancement of infrastructure design and city planning will rely heavily on 3D mapping technology as a fundamental tool.
Modeling Flood Routing with the Variable Parameter Kinematic Wave Model in Non-Prismatic Channels of Ungauged Basins
This research explores the application of the Variable Parameter Kinematic Wave Numerical Model to simulate the propagation of flood waves in non-prismatic natural waterways in ungauged basins. This study addresses issues related to irregular channel boundaries and unreliable inflow data upstream and highlights impacts associated with slope variability and changes in wetted perimeter. The inflow hydrograph is evaluated by the developed study using the SCS-CN method and a quantitative approach is adopted to analyse relationships between channel characteristics and flood wave behaviour. Five hypotheses are analysed and tested to determine if variability in channel slope and variability in wetted perimeter, effectiveness of the SCS-CN method, and an integrated approach of comprehensive performance metrics all make a difference. Agreement with observed data validates that VPKWM is reliable to model flood wave propagation. The findings suggest a need for realistic representation of channel characteristics and robust validation frameworks to improve flood management practices.
Numerical Evaluation of Subgrade Settlement with Varying Foam Mortar Thickness for Reinforcement
Soft soils are characterized by low bearing capacity, which can lead to significant settlement when subjected to loads. As the lowest layer in road construction, the subgrade must have adequate bearing strength to support the construction load. Variations in the thickness of soft soils in the subgrade can exacerbate settlement issues, making reinforcement essential. In this study, foam mortar is explored as a potential reinforcement to enhance the properties of soft soils. The aim of this research is to analyse the behaviour of soft soil embankments with foam mortar reinforcement, considering variations in the soft soil layer thickness. The analysis was conducted using the Plaxis 2D numerical model (version 2023). The model incorporated a foam mortar layer of 30 cm thickness, applied to subgrade layers with heights of 60 cm, 120 cm, and 180 cm. The study simulated a centralized load, varying from 0 to 120 KN in increments of 10 KN. The results revealed that the highest settlement occurred at the 180 cm soft soil layer, with a settlement of 0.01421 mm. Additionally, the largest deformation was observed in the soft soil layer, measuring 1.461 x 10^-3 mm demonstrates that as the thickness of the soft soil layer increases, both the settlement and deformation also rise. Foam mortar reinforcement shows promise in improving soft soil performance, but the variation in soil thickness needs to be carefully considered to minimize potential settlement issues.
Analysing Financial Risks in Public-Private Partnerships for Road Construction in Nepal Under Uncertainty
Private sector investment in transportation infrastructure, especially freeway projects, has gained significant traction in developing countries over recent decades. However, inadequate research and improper identification of criteria in many cases have led to challenges, and in some instances, project failures. The success of freeway construction projects heavily depends on the choice of a public-private partnership (PPP) model, but the economic conditions in countries like Nepal introduce various risks to these projects. This study examines the financial risks associated with PPP financing for road construction projects in Iran, using an uncertainty approach and the BAS (Best Alternative Strategy) method. The research sample included 23 experts with Ph.D. degrees in finance, all with substantial experience in research and financial consulting for investment firms. The findings indicate that political risks, coupled with the instability in government planning and program execution, significantly increase the financial risks of road construction projects. The study suggests that managing price fluctuations and enhancing bank financing options for road construction could mitigate these risks and improve the overall success of such large-scale infrastructure projects. This research highlights the need for careful planning and risk management strategies to ensure the sustainability and success of PPP road projects in Iran.
Simulation of Indoor Climate in a Church During Winter Months
The paper presents an evaluation of a heating system installed in a church, focusing on the thermal comfort provided to both churchgoers and the interior finishes. Ensuring a comfortable environment is a critical objective for designers, particularly when considering both the well-being of occupants and the preservation of the building's aesthetic elements. The heating system utilizes hydronic radiators, and its performance is assessed through Computational Fluid Dynamics (CFD) modeling. CFD simulations were conducted in Autodesk CFD, offering a detailed analysis of the system's efficiency and performance. The results highlight both the benefits and potential drawbacks of using this heating system in a church setting. One advantage is that the hydronic radiators provide a steady, even distribution of heat, which is beneficial for maintaining a comfortable interior climate. However, challenges such as uneven heat distribution in larger spaces and potential energy inefficiencies were also identified. Alternative heating systems, such as underfloor heating or forced-air systems, could offer different benefits. Underfloor heating provides even heat distribution and can be less visually intrusive, while forced-air systems may have faster response times but might be less energy-efficient or effective in preserving the interior finishes. Each of these systems presents distinct trade-offs that must be carefully considered based on the church's specific needs, including comfort, aesthetics, energy efficiency, and preservation of the building's structure and decor.
The Algorithmic Supervisor: Examining the Impact of AI-Driven Performance Management on Employee Engagement and Organizational Justice
The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) is rapidly transforming performance management systems. This paper investigates the impact of AI-driven performance management – specifically, the use of algorithmic supervisors and data analytics for employee evaluation – on employee engagement and perceptions of organizational justice. Through a mixed-methods approach incorporating a quantitative survey and qualitative interviews, we examine the relationship between AI-driven performance evaluation, employee engagement levels, and the perceived fairness of performance appraisals. The findings reveal a complex interplay, where algorithmic transparency and perceived accuracy can positively influence engagement, but lack of human oversight and concerns about data bias can erode trust and exacerbate feelings of injustice. The study concludes by offering recommendations for responsible implementation of AI in performance management, emphasizing the importance of human-centered design, ethical considerations, and continuous monitoring to mitigate potential negative consequences.
The Algorithmic Bias in Talent Acquisition: A Comparative Analysis of Machine Learning Models and Mitigation Strategies for Enhancing Diversity and Inclusion
This research investigates the pervasive issue of algorithmic bias in machine learning (ML) models used for talent acquisition. As organizations increasingly rely on automated systems to screen resumes, identify qualified candidates, and even conduct initial interviews, the potential for perpetuating and amplifying existing societal biases becomes a significant concern. This paper presents a comparative analysis of several commonly used ML models in recruitment, evaluating their performance across different demographic groups. It identifies sources of bias within these models, stemming from both data and algorithmic design. Furthermore, it explores and evaluates various mitigation strategies, including data pre-processing techniques, algorithmic adjustments, and post-processing interventions, aimed at enhancing fairness and promoting diversity and inclusion in the hiring process. The findings highlight the importance of careful model selection, robust bias detection, and proactive implementation of mitigation strategies to ensure equitable talent acquisition practices. The study contributes to the growing body of knowledge on responsible AI in HR and offers practical recommendations for organizations seeking to leverage ML for talent acquisition while upholding ethical principles
The Algorithmic Crucible: Exploring the Impact of AI-Driven Recruitment on Workforce Diversity and Inclusion
Artificial intelligence (AI) is rapidly transforming human resource management, particularly in recruitment and talent acquisition. While promising increased efficiency and objectivity, AI-driven recruitment systems raise significant concerns about their potential impact on workforce diversity and inclusion. This paper investigates the complex interplay between AI algorithms, recruitment processes, and diversity outcomes. Through a comprehensive literature review, we examine the sources of algorithmic bias, the potential for unintended discrimination, and the strategies organizations can employ to mitigate these risks. We present an empirical analysis of simulated recruitment data, demonstrating how biased algorithms can perpetuate existing inequalities. Finally, we discuss the ethical considerations surrounding AI recruitment and propose a framework for developing and deploying AI systems that promote fairness, transparency, and inclusivity in the workplace. The study underscores the critical need for proactive measures to ensure that AI serves as a catalyst for positive change, rather than a barrier to equal opportunity.
The Algorithmic Bias in Performance Management Systems: A Critical Examination of Fairness, Transparency, and Employee Perception
The increasing adoption of Artificial Intelligence (AI) and Machine Learning (ML) in Human Resource Management (HRM) has led to the implementation of algorithmic performance management systems. These systems promise increased efficiency and objectivity in evaluating employee performance. However, they also raise significant concerns regarding algorithmic bias, fairness, and transparency. This paper critically examines the potential for bias in these systems, analyzing how data biases, flawed algorithms, and lack of human oversight can lead to discriminatory outcomes. The study investigates the impact of algorithmic bias on employee perception of fairness, trust, and engagement. Through a combination of literature review, theoretical analysis, and empirical data collected from a simulated performance evaluation scenario, the paper highlights the challenges associated with implementing unbiased algorithmic performance management systems and proposes recommendations for mitigating these risks, ensuring ethical and equitable application of AI in HRM. The research aims to contribute to the development of fair, transparent, and accountable AI-driven performance management practices that foster a positive and inclusive work environment.
The Algorithmic Tightrope: Navigating Ethical Dilemmas and Bias Mitigation in AI-Driven Talent Acquisition Systems
The integration of Artificial Intelligence (AI) into talent acquisition processes promises efficiency gains and data-driven decision-making. However, this technological advancement also presents significant ethical challenges, particularly concerning algorithmic bias and fairness. This paper explores the complex landscape of AI-driven talent acquisition, examining the potential for bias to perpetuate existing inequalities in hiring practices. It reviews relevant literature on algorithmic bias, fairness metrics, and explainable AI (XAI) techniques. The study then presents a novel methodology for identifying and mitigating bias in AI recruitment systems, focusing on pre-processing techniques, in-processing constraints, and post-processing adjustments. The paper concludes by discussing the implications of these findings for HR professionals and policymakers, emphasizing the need for a proactive and ethical approach to AI implementation in talent acquisition. The importance of continuous monitoring, auditing, and human oversight to ensure fair and equitable outcomes is also highlighted.
Transforming Human Resource Management: The Impact of Technological Innovation
Technology has revolutionized HRM, shaping it into an increasingly dynamic, data-driven profession. This article assesses technology's transformative role in HRM on five different dimensions: talent acquisition, engagement, performance, AI-based decision-making, and data privacy. Qualitative by design, this research uses an interview and case study approach to probe deep into issues about how the impact of technology influences HRM practice. Findings include AI-driven recruitment, digital tools for engagement, analytics for performance optimization, and robust privacy measures, while addressing challenges such as ethical dilemmas and biases. The research underlines the criticality of integrating innovative solutions into HRM for fostering organizational efficiency and adaptability in the digital age, paving the way for future studies to explore broader contexts and mixed methodologies.
Enhancing Workplace Productivity: The Role of Employee Engagement Strategies in HR Practices
This paper looks at the tie of effective HR practices to employee engagement strategies to improve productivity. Focusing on five key elements—core HR practices, communication strategies, recognition programs, professional development opportunities, and workplace culture—the study uses qualitative methods, such as in-depth interviews and thematic analysis, to provide insights into HR practices. Findings include personalization, open communication, timely recognition, alignment of learning opportunities with career goals, and inclusive workplace culture as key drivers of engagement and productivity. Future research will look into these strategies in a wider, longer term perspective for organizational contexts.