Traditional Human Resource Management Research Articles

The Law of Data-driven on Employee Growth in Enterprise Human Resource Management in the Era of Digital Transformation

In the era of digital transformation, the popularity of artificial intelligence technology has driven the development of the field of Human Resource Management (HRM) in enterprises, and data-driven HRM has become the key to the development and success of enterprises. Due to the late start, data-driven HRM is still in the development stage in China. To address the problem that traditional HRM lacks accuracy and real-time, this study proposes a Growth Potential Prediction (GPP) model based on the combination of all class features and a Random Forest (RF) classifier. The results of the study showed that the prediction accuracy of interpersonal background, work ability, educational status and organisational environment characteristics under the screening index based on [Formula: see text]1 value were 85.01%, 66.34%, 66.41%, 44.19% and 64.27%, respectively. The accuracy of ROS under the RF classifier and the macro [Formula: see text]1 value were 87.31% and 87.24%. After adding the network features, the accuracy and macro [Formula: see text]1 values of the feature ensemble could reach 90%, and the accuracy and macro [Formula: see text]1 values of all class feature combinations were 89.01% and 89.10%, respectively. In summary, the GPP model based on the combination of all class features and the RF classifier proposed in the study can accurately predict the growth potential of employees and provide some guarantee for mining the growth pattern of employees.

Remote work and human resource management: Challenges and solutions

The rise of remote work, accelerated by global disruptions and technological advancements, has fundamentally reshaped traditional human resource management (HRM) practices. This study investigates the key challenges that organizations face in managing a remote workforce, including maintaining employee engagement, ensuring effective communication, managing productivity, and addressing mental health concerns. Additionally, the research explores the role of technology in overcoming these challenges, highlighting solutions such as virtual collaboration tools, flexible work policies, and performance monitoring systems. Through an analysis of current HR practices in organizations that have successfully adapted to remote work, this paper identifies best practices and strategic adjustments necessary for HR managers to support a distributed workforce. The findings suggest that while remote work offers flexibility and operational efficiency, it also requires a shift in HR strategies toward building a cohesive virtual work culture, offering ongoing training, and ensuring employee well-being. This research contributes to the understanding of how HRM can evolve to meet the demands of the remote work environment, providing actionable insights for organizations navigating this new paradigm.

Exploring essential features for developing a human resource management mobile application: Insights from the middle east

This study aims to investigate the key features necessary for the development of a mobile application for human resource management (HRM). It addresses the shortage of empirical studies on digital HRM in the Middle East and emphasizes the increasing importance of HRM mobile applications in the region. The research utilizes qualitative research methods, including the Delphi method, in-depth interviews, literature review, historical analysis, and critical discussion.A qualitative research strategy is employed, analyzing data from 43 HR experts and mobile app development team members from international and local businesses in the UAE and the Kingdom of Bahrain. NVivo software is used for data analysis, encompassing over 120 transcriptions, notes, and academic papers. The study identifies and explores the logical and practical features required for an HRM mobile app. It uncovers challenges related to the Delphi survey experiences, such as managing heterogeneous information and ensuring compatibility with iOS and Android platforms. This research fills a gap in the literature by investigating the digital HRM landscape and HRM mobile applications in the Middle East. It provides valuable insights from HR and technology experts, contributing to the advancement of digital HRM, mobile applications, and the application of the Delphi method methodology. The study highlights implications for industries to enhance HR operations and productivity through the adoption of mobile HR applications. Theoretical contributions include new insights into the digital HRM literature, specifically regarding the identification and discussion of essential features in HRM mobile applications. Practical implications involve the benefits of transitioning from traditional HRM to digitalized HRM in the professional era, as identified through the analyzed features of the HR app. The study offers practical insights for the development and implementation of mobile HR apps. Organizations can leverage these insights to create effective mobile HR apps that enhance HR operations and employee productivity.

The Strategy of Human Resource Management Based on the K-Means Clustering Algorithm

With the continuous accumulation of enterprise data and the continuous development of technology, data-driven decision-making has become an important trend in enterprise management. Human resource management, as an important component of enterprise management, also requires the use of data analysis tools to optimize the decision-making process. Traditional human resource management strategy algorithms are often based on experience and intuition, lacking scientific data support. These algorithms often struggle to achieve ideal results when dealing with large and complex human resource data. This study aims to provide more scientific and accurate data support for human resource management by introducing the K-means algorithm. The main research purpose of this paper is to detect and identify the data in enterprise human resource management. Data mining can realize the comprehensive detection and analysis of poetry, but the application of data mining algorithms in human resource management data processing is not much. This paper hopes to analyze the anomaly of human resource data statistics by data mining method. According to the characteristics of the K-means algorithm and DBSCAN algorithm, a hybrid K-means algorithm is proposed in this study, which can automatically generate a more appropriate K value and realize parameter self-adaptation with less manual intervention. The optimized clustering K-means algorithm obtains the initial value of DBSCAN through calculation, and then the abnormal data of human resources statistics can be obtained through the second clustering calculation. The optimized K-means algorithm can detect human resource statistics data in real time, and give a new idea for human resource analysis of data anomalies.

Embracing Duality: Exploring the Synergy of Traditional and Religious HRM Practices to Enhance Volunteer Retention in Faith-Based Organizations

ABSTRACT Volunteer retention is critical for the sustainable functioning of faith-based organizations (FBOs). This qualitative study examines the interplay of two distinct streams of Human Resource Management (HRM) within FBOs: traditional HRM practices and religious HRM practices. Drawing on data from 33 interviews conducted across four Indonesian FBOs, we explore how the integration of these practices influences volunteer retention. Our findings reveal that FBOs employ traditional HRM practices, including recruitment, task design, training, supervision, rewards systems, and managing diversity, to retain volunteers. Additionally, strategically designed religious HRM practices, encompassing spiritual individual development, spiritual leadership, shaping organizational religious culture, and managing volunteers’ relations, augment volunteer commitment. This research contributes novel insights into the nexus between secular and spiritual dimensions in volunteer retention, offering a holistic framework for FBOs to optimize volunteer management strategies and sustain their impactful missions.

Exploration on human resource management and prediction model of data-driven information security in Internet of Things

The advent of the Internet of Things (IoT) has accelerated the pace of economic development across all sectors. However, it has also brought significant challenges to traditional human resource management, revealing an increasing number of problems and making it unable to meet the needs of contemporary enterprise management. The IoT has brought numerous conveniences to human society, but it has also led to security issues in communication networks. To ensure the security of these networks, it is necessary to integrate data-driven technologies to address this issue. In response to the current state of human resource management, this paper proposes the application of IoT technology in enterprise human resource management and combines it with radial basis function neural networks to construct a model for predicting enterprise human resource needs. The model was also experimentally analyzed. The results show that under this algorithm, the average prediction accuracy for the number of employees over five years is 90.2 %, and the average prediction accuracy for sales revenue is 93.9 %. These data indicate that the prediction accuracy of the model under this study's algorithm has significantly improved. This paper also conducted evaluation experiments on a wireless communication network security risk prediction model. The average prediction accuracy of four tests is 91.21 %, indicating that the model has high prediction accuracy. By introducing data-driven technology and IoT applications, this study provides new solutions for human resource management and communication network security, promoting technological innovation in the fields of traditional human resource management and information security management. The research not only improves the accuracy of the prediction models but also provides strong support for decision-making and risk management in related fields, demonstrating the great potential of big data and artificial intelligence technology in the future of enterprise management and security.

HUMAN RESOURCE MANAGEMENT IN CHINA UNDER ECONOMIC GLOBALIZATION

Economic globalization is the development characteristic and advantage of modern economy, especially the in-depth development of knowledge economy and people-oriented era, which has become the decisive factor of the world economy in the 21st century. In this context, competition among countries increasingly revolves around the quantity, quality and development level of human resources. This transformation has brought both opportunities and challenges to human resource management (HRM) in China. Economic globalization has prompted China's human resources management concepts to be re-examined and updated, and the field of human resources management has shown a more diversified pattern. The subjects of human resources management are constantly changing, and it is necessary to adjust the human resource’s structure and improve the overall quality of the workforce. While economic globalization has brought opportunities to China, it has also brought tremendous pressure to China's human resources management. Fierce international competition has posed a severe test to human resources and challenged traditional human resource management methods and structures. This article deeply explores the impact of economic globalization on human resource management in China. It analyzes how China's human resources management responds to the emergence of diverse talent market, the growing demand for cross-cultural skills, the fluidity of global talent flows, the need for technology and digital transformation, and the regulation and compliance faced in operations challenge. Globalized environment. By understanding and responding to these changes, China's human resource management can proactively respond to the changing situation and seize the opportunities brought by economic globalization.

The impact of artificial intelligence on human resource management systems - Applications and risks

Organizations traditional human resource management model has been impacted by the ongoing optimization and advancement of artificial intelligence skills and technology, and the broadening of its application scope. The impact of artificial intelligence (AI) systems on employee recruitment, human resources allocations, and talent management is significant. This paper examines the interplay among AI, data applications, human resource management (HRM) systems and the resultant effects. It will examine the significance of effectively managing the deployment of AI systems, as existing literature defines. This study examines the effects of artificial intelligence AI technology on the effectiveness of company administration compared to traditional human resource management systems (HRMS). Several recommendations are offered to enhance the reformation and optimization of the organizations human resources (HR) division. The research findings indicate that incorporating the new system in conjunction with human involvement can significantly enhance the efficiency of employee recruitment, allocation of human resources, and management of talent within the firm. There was an improvement observed in both employee happiness and productivity elements.

PROPHETIC PRACTICES IN HUMAN RESOURCE MANAGEMENT

Traditionally, the main motive behind focusing on human resources is organizations’ striving to achieve the maximum profits possible. While the Islamic motives are focusing on caring about the human resource as a human being whom was created to be Allah's deputy on earth. Prophet Mohammed PBUH came with a message that made working have a religious value. Separating work from its Islamic values stopped it from being the biggest motive for human’s development in organizations. Prophet Mohammed PBUH came with a message that honored humans and made their practical development a great concern, motivating them to building values, behaviors, improving performance, achieving and managing goals. The Prophet PBUH came with great values that would advance the idea of human resources management and enhance the human being in the organization, which would reflect on his performance and achieving the goals and objectives of organizations. This research claims that the impact of the prophetic applications on human resources is more comprehensive, spacious in scope and has greater impact than traditional human resources management. This research can be a scientific and practical reference to highlight the mechanisms and practices found from Prophet’s sayings, actions, or from the Sharia he brought in.

The impact of artificial intelligence technology on the management of front-line employees in manufacturing enterprises: The proposal of a theoretical model

Artificial intelligence technology has greatly changed production process equipment and technological systems. The changes in production technology systems and environments will inevitably affect people's psychological behavior, thereby affecting employee performance. This article found that artificial intelligence technology has the effects of interpersonal isolation, skill deprivation, innovation opportunity deprivation, and sense of achievement deprivation on frontline employees in manufacturing enterprises, leading to a sense of powerlessness and fatigue among frontline employees, resulting in a decrease in employee satisfaction, an increase in turnover rate, and an increase in insecurity. To address the impact of artificial intelligence technology on frontline employees, traditional human resource management models should be changed, employee care should be strengthened, team activities should be emphasized, life situations should be created, and diverse skills should be developed.

Faculty matter: development of faculty diversity climate and its relation to graduate student intent to stay

ABSTRACT This study sought to advance our understanding of the nexus between policy and ambient discrimination experiences of faculty and the intention to stay among graduate students. To accomplish this, we built on theory and research in higher education by leveraging models from the strategic human resource management (HRM) literature. In doing so, we advanced a novel theoretical model relating faculty perceptions of traditional HRM policies and ambient discrimination to department-level shared perceptions of diversity climate. We further theorized how that shared diversity climate relates to evaluations of graduate students’ general campus climate and their intent to stay. We tested this model using a unique multisource, multilevel dataset comprised of 266 university faculty and 569 students who were pursuing graduate degrees across 28 departments at a midsized university located in the Midwest. The results supported the hypothesized model and established the important relationship between the diversity-related work environment experienced by faculty and the experiences and highly consequential outcomes for students and higher education. By bridging the gap between faculty work experiences and student perceptions and retention this study provides both new theoretical insights and important practical suggestions.

Optimization of university human resource management system on faculty members with anxiety

BackgroundDue to outdated work concepts and unscientific performance evaluations, the enthusiasm of university faculty members is not high, and some faculty members experience work anxiety, which affects the normal operation of work. The traditional human resource management system can no longer meet the needs of human resource management in universities and needs to be optimized. In order to stimulate the work enthusiasm of faculty and alleviate their psychological anxiety, research is being conducted to optimize the human resource management system in universities.Subjects and MethodsThe study selected 60 employees with work anxiety and divided them into a control group and an intervention group, with a total of 30 individuals. The former adopts a traditional human resource management system, while the latter adopts an optimized human resource management system with a duration of 6 months. Employees were evaluated using indicators such as the Hamilton Anxiety Scale (HAMA), and SPSS23.0 software was used for data processing.ResultsAfter 6 months, there was a significant difference in work psychological anxiety between the two groups of teaching staff. Compared with the control group, the HAMA score of the intervention group was significantly lower than that of the control group (P<0.01).ConclusionsIt needs to optimize the human resource management system in universities, improve performance evaluation methods, stimulate the enthusiasm of faculty, and ensure that their efforts are treated fairly, then resulting in a significant improvement in their work anxiety.

Enhancing industrial environmental performance: interplay among environmental sustainability, green HRM, and green competitive advantage.

This study highlights the importance of green human resource management (HRM) practices, which investigates the connection between environmental sustainability and green competitive advantage. Using the CGM model and the generalized method of moments (GMM), this research investigates how green HRM may foster environmental sustainability by creating a competitive advantage for businesses. Research shows that green HRM may achieve green competitive advantage practices, crucial for environmental sustainability. By "green HRM practices," we mean incorporating environmental concerns into traditional HRM processes, including hiring, training, evaluating, and compensating employees. However, the importance of green HRM practices cannot be overstated due to their efficiency in creating a competitive advantage and advancing environmental sustainability. Specifically, the research shows that businesses that have adopted "green HRM practices" outperform their competitors in terms of environmental sustainability. The last section emphasizes the importance of green HRM as a key facilitator of environmental sustainability. Implications for businesses that want to improve their environmental performance are also provided.

Resource management projects in entrepreneurship and retain customer based on big data analysis and artificial intelligence

Retaining clients is turning into an estimation center in an industry with expanding rivalry. Because it is difficult to keep customers and easy for them to switch brands, the idea of customer retention has become the subject of research in the sales industry. Traditional human resource management systems are unable to manage and analyze data because of the rapid growth of enterprise-generated data's processing capacity. This exploration proposes novel strategy in human asset the executives for little new company business with their client hold utilizing Artificial intelligence (AI) procedures. Behavioral pattern analysis based on reinforcement radial fuzzy decision with quadratic kernel vector machine is utilized here for human resource management and customer relationship retention. In terms of prediction accuracy, area under the curve (AUC), average precision, sensitivity, and quadratic normalized square error, various human resource datasets based on entrepreneurship are the subjects of the experimental analysis. The proposed technique attained prediction accuracy of 98%, AUC of 89%, average precision of 83%, sensitivity of 66%, quadratic normalized square error of 59%.

The Context Matters to the Exclusive Talent Management: How to Measure and Pay in South Korea

Talent Management (TM) has been derived from Western, mainly the US and there are gaps between westernized TM format and contexts of non-Western countries when they practice Western-format TM. Therefore, this article aims to explore South Korean-style local TM practices through a view of Resource-Based View. The local practices are also compared to foreign firms in South Korea in four areas: talent identification via performance evaluation system, South Korean style payment structure to include performance-related pay dispersion, non-monetary rewarding and training and development. This article conducted 55 semi-structured interviews from employees who are working for local and foreign companies in South Korea. TM practices in large local firms were more discriminative, and monetary focused rewards than foreign firms. Talent development programmes (e.g., a job rotation) were not practiced in local firms but in foreign firms. The TM practices are influenced by the local context and the traditional human resource management philosophies.

Volunteers in Public Service Production: Modeling the Contributions of Volunteers to Organizational Performance

Abstract Volunteer use as an alternative service delivery arrangement entails public organizations directly incorporating volunteers in service production through a quasi-employment relationship. However, research evaluating the contributions of volunteer labor to organizational performance are relatively few. This article fills this gap by drawing from two theoretical insights. First, this study tests a linear model based on the traditional human resource management approach of interchangeability in which volunteers constitute a one-on-one substitute for paid employees. Second, this study draws insight from critical mass theory to explore a nonlinear model based on the assumption that the productivity of one volunteer may not be equal to that of one paid employee due to the systematic limitations of volunteer labor. Analyzing a five wave panel dataset of 484 local law enforcement agencies in the United States that utilize sworn volunteers with general arrest powers, the findings indicate that an increase in the number of sworn volunteers has a linear negative effect on clearing violent crimes in sheriffs offices. However, the results also indicate evidence of curvilinear effects on clearing violent crimes in police departments. This article suggests the need to account for the systematic limitations of volunteer labor when measuring performance and to explore multiple indicators. This study contributes to theory building and testing by enabling a two-way understanding of the relationship between volunteer involvement and organizational performance in the public sector.

Cloud Inspired Human Resource Management Empowered with Fuzzy Inference System

Traditional Human Resource Management (HRM) systems face complications in managing various issues related to personnel, organization, salary, attendance, etc. Cloud Computing (CC) provides help in these business applications. This research proposes a Cloud-Inspired Human Resource Management Information System (CIHRMIS) employing a fuzzy logic system. The proposed CIHRMIS, in view of Fuzzy Logic, may help companies accomplish their HRM tasks proficiently, which in turn reduces the cost and increases managerial efficiency. Further, the Fuzzy Logic System performs adaptive activities, which can be used with multiterminal platforms operation, which supplements the application value of the system as well.

Challenges and prospects of human resource management under the background of big data

With the development of information technology and Internet computing, big data has led individuals and enterprises to step into the era of automation in many ways. Big data technology usually starts with the classification and large-scale uptake of resource information, and then integrates, stores and utilizes the information displayed in the data after certain screening processing. The value of data sets is diverse, so it often requires more scientific and systematic classification analysis and computational processing design than traditional classification to make the resource set play an ideal role. Big data has many characteristics, the core of which is extensive information sources, rich classification, high gold content, fast and efficient analysis and processing process, and can simultaneously take into account a wide range of coverage and fine direction.
 No matter in the enterprise or the market, it is generally believed that human resources are the primary resources to help economic progress. Human resource management includes a large amount of personal information of enterprises and employees. On this basis, big data can also help enterprises to better develop human resource modules by virtue of its excellent information collection and processing ability. In order to transfer from the traditional human resources management and control means to a more efficient way, it is necessary to achieve reasonable evaluation while taking into account the breadth and depth of information sources, introduce and further cultivate high-end employees through screening mechanism, and down-to-earth analysis of all aspects of the integration of big data and human resources management. This paper analyzes the existing problems, application advantages, challenges and innovations of the connection between human resource management and big data, and summarizes the various foundations that human resource management should have under the background of big data technology.

Human Resource Management in the Internet Era

In the Internet era, enterprises are paying more and more attention to human resource management, and it is crucial for enterprises to use Internet technology to enhance and improve human resource management. This paper analyzes the new opportunities that human resource management will face in the Internet era, puts forward the main problems of traditional human resource management in enterprises and gives improvement measures.

The HRM Model Based on Competency Model in the Context of New Age Intelligence

In the new era of rapid technological development, new technologies are emerging: mobile positioning technology, face recognition technology, two-dimensional code technology, artificial intelligence technology, etc. In the trend of the times, these emerging technologies continue to penetrate into people’s daily life and influence their social habits. At present, the shadow of emerging technologies such as artificial intelligence has already appeared in human resource management and organizational behavior, and the application of these emerging technologies will certainly promote the change of human resource management and organizational behavior and have a double impact on human resource management and organizational behavior. On the one hand, emerging technologies such as artificial intelligence bring new opportunities for the development of human resources, which can not only improve the efficiency of human resource management, optimize human resource organizational behavior, and accelerate the transformation of enterprise management mode. On the other hand, the application of these emerging technologies also brings new challenges for HR development and management: the enterprises’ demand for low-end positions decreases, increasing the employment pressure in the talent market; the enterprises’ development faces transformation and rectification, increasing the difficulty of survival of small enterprises; the employment difficulties and development prospects of HR departments are uncertain. In the context of the new era of intelligence, the impact of emerging technologies on human resource management and organizational behavior is bound to intensify, and enterprises should innovate ideas and integrate new ways and methods to upgrade the traditional human resource management model and seize the opportunity to promote human resource management changes, so that emerging technologies such as artificial intelligence can inject new vitality into modern human resource management. Based on this, this paper proposes an enterprise HR management strategy based on the competency model and combines it with a practical case study and finds that the strategy has been recognized by 91% of employees within the company and has effectively improved the efficiency of HR management.