Purpose: The purpose of this paper is to examine the relationship between workplace incivility and knowledge hiding, and role of personality disposition (neuroticism) in moderating such relationships. Design/methodology/approach: Data were collected from 108 employees nested in 18 teams from private sectors via survey questionnaire. Confirmatory factor analysis and hierarchical regression models were used to test the hypotheses. Findings: The findings show that the higher the level of workplace incivility experienced by the team members, the higher the tendency for them to hide knowledge and this relationship is moderated by neuroticism. Specifically, the relationship was found to be stronger for those employees high in neuroticism compared to those low in neuroticism. Practical implications: The study offers important implication in term of knowledge hiding prevention or reduction. The behavior can be reduced by creating awareness among employees on the importance of civility at work via campaign, realistic job preview and leading by example. To manage the effect of neuroticism, managers need to identify those high in the trait and provide them with training on how to better regulate and manage negative emotions in the workplace. Originality/value: The study contributes to the research on knowledge hiding behavior by advancing the understanding of organizational and personal factors that can influence knowledge hiding among employees working in team. It is the first to propose and empirically validate the predictive effect of workplace incivility on knowledge hiding. It also addresses the usefulness of examining personality disposition in understanding the relationship between workplace incivility and knowledge hiding behavior.

Purpose: Grounded on substantial theories and service concept models, this paper aims at proposing a novel patient satisfaction index (PSI) model. This model allows for identifying the key sources of patient satisfaction and for comparing performance results across hospitals competing on the national and international health-care market. The PSI is also an important benchmark instrument for medical tourism. Design/methodology/approach: The PSI model was initially tested in hospitals in a developed country (the German hospital market). A total of 1,281 complete patient responses were analyzed by means of partial least squares structural equation modeling (PLS-SEM). Findings: This model’s sources of competitive advantage – doctors’ and nurses’ interaction quality, environmental quality and outcome quality – mostly explain patient satisfaction, which is the key target construct. The results also reveal that the doctors’ and nurses’ interaction quality is the most salient driver of patient satisfaction. Practical implications: The proposed PSI model can be generally applied to facilitate the comparison of hospitals on a regional, national and even an international level. In addition, the PSI model helps health-care management facilities prioritize their service components’ improvement (e.g. to attract more medical tourists). Originality/value: Medical tourists are confronted with a variety of health destinations and may find it difficult to choose an appropriate region and health-care provider. This study contributes to medical tourism research by developing a general patient satisfaction index model for medical tourists.

The present study addresses the mediating role of social support in the relationship between employee resilience and employee engagement. A cross-sectional design was adopted to collect data from a sample of 260 registered nurses working in public hospitals in Lagos, Nigeria. The findings suggest that employee resilience is significantly related to social support and employee engagement. However, the quantitative analysis could not establish a significant mediation role of social support in the relationship between employee resilience and employee engagement. Based on the results, this research provides empirical evidence for the importance of employee resilience to greater employee engagement.

Eldercare workers’ turnover intentions have caused serious concern given their high demand and pivotal role in ensuring elderly individuals’ well-being. This systematic review examined the main factors of eldercare employees’ turnover intentions with the purpose of identifying gaps and structure a novel human resource (HR) approach framework for eldercare social enterprises through a global literature review and realistic conclusions. A total of 29 publications appeared between 2015 and 2021 were digitally extracted from six databases and are extensively discussed in this review. Resultantly, eldercare workers’ turnover intentions were positively impacted by job burnout, low job motivation, and restricted job autonomy. The findings of this study correspond to those of past literature, which highlighted the necessity of thoroughly examining eldercare worker retention practices from an organisational (HR) perspective. Furthermore, the current study outlines the factors influencing eldercare workers’ turnover intentions as well as determine proper HR approaches to mitigate employee turnover issues among eldercare workers for organisational sustainability.

The integration of progressive 4.0 technology into human resource management (HRM) represents a significant shift in how organizations optimize and enhance workforce capabilities. This review explores the origins, applications, benefits, challenges and future prospects surrounding smart HRM 4.0. By reviewing existing literature, this paper examines the utilization of Artificial Intelligence (AI), Big Data analytics, machine learning (ML) and the Internet of Things (IoT) in HRM, specifically in talent acquisition, training, performance management, and rewards. Additionally, it addresses the implementation challenges including data quality assurance, skill shortages, and cultural resistance. The paper also emphasizes the importance of ethical considerations. In terms of future research, it highlights the necessity for ethically deploying Industry 4.0 and establishing robust AI governance frameworks. By combining technological innovation with ethical values, organizations can navigate the complexities of this integration, leading to a future characterized by workplace efficiency, accountability, and fairness.