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The purpose of this study is to explore the mechanism of digitalized transformation in organizations’ human resource management (HRM). This study summarizes three basic factors driving the digital transformation process in China: level of perception, level of application and speed of transformation.

This study analyzes the strategic transformation process of HRM in Haier, Hisense and Chambroad to explore the human resource digital transformation mechanism in Chinese enterprises.

The results of this study show that three HR value chain models can be constructed based on how well HRM deals with business: the efficiency-oriented HRM value chain, quasi-business-oriented HRM value chain and business-oriented HRM value chain. The basic factors – level of perception, level of application and speed of transformation – are observed in the entire HRM digital transformation process.

This study provides theoretical and empirical insights for enterprises to explore the value of digital technology in HRM and facilitate the digital transformation of HRM.

The study aims to explore digital transformation from the viewpoint of human resource management to uncover possible threads of relationship using bibliometric analysis. It also aims to identify the trending research themes within the domains of digital transformation (DT) and human resource management (HRM) collectively.

The research employs a mix of quantitative bibliometric techniques and qualitative content analysis. A corpus of 227 articles retrieved from the Scopus database was analyzed using the R-based Biblioshiny and VOS viewer.

The study shows publication trends, influential authors, leading journals, highly productive institutions, and, countries in the domain of DT and HRM. Co-citation and co-occurrence analysis was undertaken to identify the research clusters, depicting trending research themes that extensively dominate the research under this domain.

This study will serve as a ready reckoner for academicians and business leaders, giving them useful insights to make their road towards digital transformation less challenging with the assistance of human capital.

This study is one of the initial efforts to quantitatively synthesize the results of earlier publications using bibliometric techniques in the domain of DT and HRM together. It will aid researchers in locating research gaps and filling those gaps in the future.

Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

This study aims to identify the enablers of human resource (HR) digitalization and HR analytics. This paper also aims to build a relationship map using interpretive structural modeling.

A systematic literature review is used to identify the key enablers of HR digitalization and HR analytics. Ten expert opinions have been taken from the key officials of IT firms located in New Delhi North Central Region.

This study is focused on the enablers of HR analytics. It is found that change management (CM) in the organization is the key enabler of implementing HR digitalization and analytics in an organization. However, other elements like learning culture, training and development, E-learning management and HR transformation (HRT) play a vital role in implementing HR analytics. It is also found that implementing artificial intelligence for HR practices is the ultimate goal for every organization.

Management teams in IT firms should focus on the continuous learning process in the organization. The CM should be expedited for digitalization and adoption of HR analytics. Managers must go through the ramification of HRT, which possesses diligence in HR analytics and artificial intelligence.

This study explicitly talks about the enablers of HR digitalization and HR analytics. It also explores the relationship between the enablers. This study also describes the driving and dependence power of all the enablers.

HR analytics is a process for systematic computational analysis of data or statistics. It discovers, interprets and communicates significant patterns in data to enable evidence-based HR research and uses analytical insights to help organizations achieve their strategic objectives. However, its adoption and utilization among HR professionals remain a subject of concern. This study aims to determine the reasons that facilitate or inhibit the acceptance of HR analytics among HR professionals in the banking, financial services and insurance (BFSI) sector.

A sample of 387 HR professionals in BFSI firms across India was collected through non-probabilistic purposive sampling. Structural equation modeling was applied to analyze the association between predetermined variables. In addition, the predictive relevance of “Data Availability” was analyzed using hierarchical regression.

The results revealed that data availability, hedonic motivation and performance expectancy positively influenced behavioral intention (BI). In contrast, effort expectancy, social influence and habit had an insignificant effect on BI. Also, facilitating conditions (FCs), habit, BI achieved a variance of 60% in HR analytics use. The use behavior of HR analytics was significantly influenced by FCs and BIs.

This study focuses on insights into the elements that influence HR analytics adoption, revealing additional light on success drivers and grey areas for failed adoption.

This research adds to the body of knowledge by identifying factors that hinder the adoption of HR analytics in Indian organizations and signifies the relevance of easy accessibility and availability of data for technology adoption.

This paper explores the transformative impact of regular employee feedback and recognition in the workplace. This study aims to provide a comprehensive understanding of the role of feedback in fostering a culture of accountability and appreciation, thereby improving organizational effectiveness and employee satisfaction.

The paper adopts a qualitative approach, drawing on extensive professional experience and contemporary Human Relations practices. It synthesizes insights from various employee engagement strategies, feedback mechanisms and recognition programs implemented in diverse organizational settings. The methodology includes an analysis of best practices in feedback delivery, the role of technology in Human Relations and the challenges of implementing effective feedback systems. The approach is grounded in practical Human Relations expertise, offering a real-world perspective on managing employee engagement.

This paper finds that clear communication of expectations, setting achievable goals, providing resources and acknowledging success are key to effective feedback. The study also reveals the importance of personalized, empathetic feedback approaches and the strategic use of technology in HR processes.

The insights presented are based on the author’s extensive experience and existing literature, which may not encompass all possible scenarios in diverse organizational contexts. Future research could benefit from empirical studies to validate these findings across different industries and company sizes.

This paper offers actionable strategies for HR professionals and managers to enhance employee engagement through effective feedback and recognition. The paper also discusses the integration of technology in feedback processes and the importance of ongoing training for effective feedback delivery. These insights are valuable for organizations aiming to foster a positive work environment and high employee morale.

The study highlights the social dimension of workplace feedback, emphasizing its role in building a supportive and inclusive work culture. It underscores the importance of recognizing diverse feedback preferences and the impact of empathetic, personalized communication on employee well-being and job satisfaction. The paper advocates for feedback practices that contribute to a more engaged, motivated and socially connected workforce.

This paper offers a unique blend of practical HR expertise and strategic insights into employee feedback and recognition. It fills a gap in the literature by providing a comprehensive view of the impact of feedback on employee engagement and organizational performance. The paper’s value lies in its practical applicability and its contribution to the understanding of effective feedback strategies in modern workplaces.

While the potential of human resource practices (HRPs) for promoting performance is widely recognized, even though crucial, employees’ assessment of HRPs remains under-researched, especially in emerging economies. Hence, the purpose of this research is to examine the influence of employee satisfaction with HRPs on job performance through the mediating role of job dedication (JD) and the moderating role of incentive gamification.

The current research adopts a quantitative method. Specifically, using a questionnaire survey, 418 valid responses collected (through purposive sampling) via cross-sectional method from the employees of Nigerian Information and Communication Technology (ICT) firms were used to test the research hypotheses empirically.

The results revealed that satisfaction with HRPs has a positive influence on job performance. Satisfaction with human resource practices has a positive influence on job dedication. Job dedication has a positive influence on job performance. The link between employees’ satisfaction with human resource practices and job performance is mediated by job dedication. The link between satisfaction with human resource practices and job dedication is moderated by incentive gamification, that is, the positive link is stronger when incentive gamification is high. The link between satisfaction with human resource practices and job performance is moderated by incentive gamification, that is, the positive link is stronger when incentive gamification is high.

The current study highlights the importance of employees’ assessments of human resource practices, which may be used to promote employee dedication, which in turn results in improved performance. The findings are helpful to open the black box of the impact of satisfaction with HRPs on job performance. The results also offer important new valuable guidance for practitioners and will aid the management of human resource practice design, audits, and communication.

This paper aims to provide an elucidative and explanatory overview of decision-making theory that human resource management and development (HR) researchers and practitioners can use to explore the impact of heuristics and biases on organizational decisions, particularly within HR contexts.

This paper draws upon three theoretical resources anchored in decision-making research: the theory of bounded rationality, the heuristics and biases program, and cognitive-experiential self-theory (CEST). A selective narrative review approach was adopted to identify, translate, and contextualize research findings that provide immense applicability, connection, and significance to the field and study of HR.

The authors extract key insights from the theoretical resources surveyed and illustrate the linkages between HR and decision-making research, presenting a theoretical framework to guide future research endeavors.

Decades of decision-making research have been distilled into a digestible and accessible framework that offers both theoretical and practical implications.

Heuristics are mental shortcuts that facilitate quick decisions by simplifying complexity and reducing effort needed to solve problems. Heuristic strategies can yield favorable outcomes, especially amid time and information constraints. However, heuristics can also introduce systematic judgment errors known as biases. Biases are pervasive within organizational settings and can lead to disastrous decisions. This paper provides HR scholars and professionals with a balanced, nuanced, and integrative framework to better understand heuristics and biases and explore their organizational impact. To that end, a forward-looking and direction-setting research agenda is presented.

In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study aims to propose the reconfiguration of assembly systems by incorporating multiple humans with robots using a human–robot task allocation (HRTA) to enhance productivity.

A human–robot task scheduling approach has been developed by considering task suitability, resource availability and resource selection through multicriteria optimization using the Linear Regression with Optimal Point and Minimum Distance Calculation algorithm. Using line-balancing techniques, the approach estimates the optimum number of resources required for assembly tasks operating by minimum idle time.

The task allocation schedule for a case study involving a punching press was solved using human–robot collaboration, and the approach incorporated the optimum number of appropriate resources to handle different types of proportion of resources.

This proposed work integrates the task allocation by human–robot collaboration and decrease the idle time of resource by integrating optimum number of resources.