Search results

More attention should be paid to human resource management practices, as they play a vital role in the retention of the skilled workforce for improved competitive advantage and reduced skill shortage. This study aims to examine the impact of engineers' satisfaction regarding fairness in key human resource management practices such as performance management, compensation and pay, and employee relations on their intention to stay in Indian construction firms.

This research was undertaken using a questionnaire survey conducted among 230 engineers working in Indian construction firms. Data collection was done by using self-administered questionnaires. The quantitative analysis of the collected data was carried out. The constructs involved in the study were validated using factor analysis. The correlation and regression analyses were used to examine the relationship between engineers' satisfaction with fairness in human resource practices and their intention to stay.

Results showed that satisfaction with fairness in human resource practices, such as performance management and employee relations are positively related to engineers' intention to stay, whereas satisfaction with fairness in employee relation practices highly predicts engineers' intention to stay.

This study adds to the body of knowledge by examining the impact of engineers' satisfaction with fairness in human resource practices on their intention to stay in the Indian construction sector, which is an under-researched area. Satisfaction with fairness in employee relation practices is identified as the strongest predictor of engineers' intention to stay. The finding of the research could help construction companies develop human resource practices and policies to promote the retention of construction professionals, particularly engineers, who work for them.

One of the criticisms that can be addressed to the existing HRM literature is that performance is often the primary target, leaving well-being as a secondary consideration. This study aims to put employee well-being at the center of HRM concerns. By focusing on needs-supply fit and social exchange theories, our approach focuses on employees’ perceptions of the effectiveness and fairness of HRM practices.

Based on a sample of 740 workers collected via an electronic survey, HRM practices were grouped into bundles using factor analysis to form an HRM system. The impact of the HRM system and its bundles on employee well-being and job performance was analyzed using structural equation modeling (SEM). The mediating role of well-being was tested with Stata’s medsem package.

The HRM system and its bundles (Include, Care, Reward and Enhance) derived from the perceived effectiveness and fairness of HRM practices have a positive direct effect on employee well-being and a positive indirect effect on job performance through the mediating role of well-being. However, the bundles have no direct effect on job performance, highlighting the importance of integrating employee well-being into HRM concerns.

These findings reveal that when employees consider HRM practices to be fair and effective, it promotes their well-being, which has a positive impact on their job performance.

This study examines the direct impact of justice perceptions of performance appraisal practices (procedural, distributive, interpersonal and informational justice) on job satisfaction, intention to stay and job engagement. Further, it investigates the effect of job engagement on job satisfaction and intention to stay. Moreover, the study tests the mediating role of job engagement on the impact of justice perceptions of performance appraisal practices on job satisfaction and intention to stay.

A total of 650 self-report structured questionnaires were distributed among the employees of 50 information technology companies, and 503 samples were received. Partial least square-structural equation modeling was used to test the hypothesized model.

This study revealed that justice perception of performance appraisal practices positively affects job satisfaction, intention to stay and job engagement. In addition, job engagement positively affects job satisfaction and intention to stay. Further, job engagement significantly transfers the impact of justice perceptions of performance appraisal practices on job satisfaction and intention to stay, thus confirming the mediating role of job engagement. However, the significant direct impact of justice perceptions of performance appraisal practices on job satisfaction and intention to stay in the presence of a mediator, i.e. job engagement, revealed partial mediation.

The findings of this study augment the social exchange theory by explicating that an individual who perceives justice in performance appraisal practices is likely to have greater job engagement, which ultimately leads to higher job satisfaction and intention to stay. This study filled the research gap by examining the role of four justice components of performance appraisal practices on job satisfaction and intention to stay and the mediating role of job engagement in transferring the impact of justice perceptions of performance appraisal practices on job satisfaction and intention to stay.

This study showed the importance of four justice components of performance appraisal practices in enhancing employee job engagement. Hence, this study would motivate information technology companies to maintain fairness in performance appraisal practices to enhance employee job engagement and ultimately increase job satisfaction and intention to stay.

This study is one of its kind that tested the direct impact of comprehensive justice components (procedural, distributive, interpersonal and informational justice) of performance appraisal practices on job satisfaction and intention to stay. In addition, this is a unique study that examined the mediating effect of job engagement on the impact of justice perceptions of performance appraisal practices on job satisfaction and intention to stay.

In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must ensure that their star performers believe that company’s reward and recognition (R&R) system is fair and equal. This study aims to use an explainable machine learning (eXML) model to develop a prediction algorithm for employee satisfaction with the fairness of R&R systems.

The current study uses state-of-the-art machine learning models such as Naive Bayes, Decision Tree C5.0, Random Forest and support vector machine-RBF to predict employee satisfaction towards fairness in R&R. The primary data used in the study has been collected from the employees of a large public sector undertaking from an emerging economy. This study also proposes a novel improved Naïve Bayes (INB) algorithm, the efficiency of which is compared with the state-of-the-art algorithms.

It is seen that the proposed INB model outperforms the state-of-the-art algorithms in many scenarios. Further, the proposed model and feature interaction are explained using the explainable machine learning (XML) concept. In addition, this study incorporates text mining techniques to corroborate the results from XML and suggests that “Transparency”, “Recognition”, “Unbiasedness”, “Appreciation” and “Timeliness in reward” are the most important features that impact employee satisfaction.

To the best of the authors’ knowledge, this is one of the first studies to use INB algorithm and mixed method research (text mining along with machine learning algorithms) for the prediction of employee satisfaction with respect to the R&R system.

The paper aims to examine the effect of justice perceptions of performance appraisal (JPPA) practices (i.e. distributive, procedural, informational and interpersonal justice) on organizational citizenship behavior (OCB) and affective commitment (AC) and the effect of AC on OCB. Further, it investigates the mediating role of AC in the relationship between JPPA practices and OCB. Moreover, this study examines the moderating effect of job level on the relationship between JPPA practices and OCB.

The data were collected using a self-reported structured questionnaire. A total of 650 questionnaires were distributed among the employees of 50 information technology (IT) companies in India, and 503 samples were obtained. The conceptual framework was tested using the partial least squares structural equation modeling (PLS-SEM) method, and the moderating effect was tested using process macro.

The findings of this study reveal that the JPPA practices positively affect OCB and AC and AC affects OCB. Further, AC partially mediates this relationship between JPPA practices and OCB. Furthermore, the direct effect of JPPA practices on OCB happens to be strengthened when the job level decreases, thus confirming the moderating role of job level.

The findings of this study augment the social exchange theory (SET) by suggesting that individuals perceiving justice or fairness in performance appraisal practices are likely to have a greater AC that ultimately engages employees in OCB.

This study will be helpful for human resource practitioners in IT companies who are responsible for the fairness of performance appraisal practices and expect their employees to be emotionally attached to the organization and engaged in OCB.

The study adds to the body of knowledge of how justice in performance appraisal practices links to OCB through AC and moderates by job level in an emerging economy in Asia.

The research paper aims to analyse the scholarly literature on advancing HR analytics as an intervention for attrition, a problem that lingers on organisational performance. This study aspires to provide an in-depth literature review and critically assess the knowledge gaps in HR analytics and attritions within organisational performance.

The review analyses the corpus of 196 research articles published in ostensible journals between 2011 and 2023. To identify research gaps and provide valuable insights, this study synthesises relevant studies using School of thought (S), Context (C), Methodology (M), Triggers (T), Barriers (B), Facilitators (F) and Outcomes (O) (SCM-TBFO framework). This study employs the R programming language to conduct a systematic literature review in accordance with the “preferred reporting items for systematic reviews and meta-analysis” (PRISMA) guidelines.

The emerging discipline of HR analytics encompasses the potential to manage attrition and drive organisational performance enhancements effectively. The study of SCM-TBFO encompasses a multidimensional approach, incorporating diverse perspectives and analysing its complex aspects compared to various approaches. The School of thought includes the human capital theory, expectancy theory and resource-based view. The varied research contexts entail the USA, United Kingdom, China, France, Italy and India. Further, the methodologies adopted in the studies are artificial neural networking (ANN), regression, structure equation modelling (SEM) case studies and other theoretical studies. HR analytics and attrition triggers are data mining decision systems, forecasting for firm performance and employee satisfaction. The barriers include leadership styles, cultural adaptability and lack of analytic skills, data security and organisational orientation. The facilitators were categorised into data and technology-related facilitators, human resource policies and organisational growth and performance-related facilitators. The study's primary outcomes are technology adoption, effective HR policies, HR strategies, employee satisfaction, career and organisational expansion and growth.

The primary goal of the literature review is to provide a comprehensive overview of the current state of HR analytics and its impact on organisational performance, particularly in relation to attrition. Further, the study suggests that attrition, a critical organisational concern, can be effectively managed by strategically utilising HR analytics and empowering data-driven interventions that optimise performance and enhance overall organisational outcomes.