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Most prior studies treated human resource management (HRM) strength as a whole, while neglecting the dynamic interactions between distinct components (consensus, consistency and distinctiveness). The authors lack a deep understanding of how different components operate together to influence burnout. To address these gaps, this study aims to adopt signaling theory to investigate the interactions among different components and their impacts on employee burnout.

The authors collected time-lagged data from 231 full-time employees in manufacturing firms in Suzhou, China. The authors used the PROCESS Model 6 and hierarchical multiple regression to analyze the data.

This study found that HRM system consensus and consistency mitigate employee burnout, whereas HRM distinctiveness is not significantly related to burnout. Furthermore, the authors revealed that HRM system consistency (rather than distinctiveness) mediated the relationship between consensus and burnout. Moreover, the authors found the sequential mediating effects of HRM system distinctiveness and consistency on the association between consensus and burnout.

Considering that employees’ well-being problems may be debilitating and overwhelming during the COVID-19 pandemic, it is particularly ethical and timely for managers to direct attention to the role of HRM system strength in addressing employee burnout.

This study advances the HRM system literature by teasing out the interactions between the three pivotal components of HRM strength. Our study is among the first to empirically investigate the internal relationships between the meta-features of the HRM system and employee burnout. In doing so, the authors develop a more nuanced understanding of the collective nature of a strong HRM system that conveys a shared message about HRM to promote well-being.

This study extends extant literature by establishing an integrative framework connecting different forms of HRM attributions (internal HRM well-being attributions and exploiting attributions, and external Labor Law attributions) and a specific single meta-feature of HRM system strength (consistency) to employee well-being.

In total, 279 paired and valid responses from eight manufacturing firms located in three cities in China were analyzed in this two-wave study. PROCESS macro tool was used to examine the mediating role of thriving at work and the moderating role of HRM system consistency in the relationship between HRM attributions and thriving at work.

Thriving at work mediated the relationship between internal HRM exploiting attributions, external Labor Law attributions and employee well-being. On the other hand, internal HRM well-being attributions did not indirectly influence employee well-being through thriving at work. HRM system consistency moderates the association between internal HRM attributions (rather than external Labor Law attributions) and thriving at work.

This research is only concerned with a particular form of external attributions in one country. In fact, there is a wide range of other external HRM attributions (e.g. organizational intention to imitate their competitors in today’s global economy).

Managers should understand that managing the well-being of the workforce is an important part of HRM for responsible organizations and make efforts to improve employees’ affective-motivational states.

The authors offer insights into HRM attributions research by differentiating internal attributions from external Labor Law attributions based on their disparate implications for employee well-being.

Based on the JD-R model and process-focused HRM perspective, this research paper aims to investigate the processes underlying the relationship between AI-enabled HR analytics and employee well-being outcomes (resilience) that received less attention in the AI-driven HRM literature. Specifically, this study aims to examine the indirect effect between AI-enabled HR analytics and employee resilience via job crafting, moderated by HRM system strength to highlight the contextual stimulus of AI-enabled HR analytics.

The authors adopted a time-lagged research design (one-month interval) to test the proposed hypotheses. The authors used two-wave surveys to collect data from 175 full-time hotel employees in China.

The findings indicated that employees' perceptions of AI-enabled HR analytics enhance their resilience. This study also found the mediation role of job crafting in the mentioned relationship. Moreover, the positive effects of AI-enabled HR analytics on employee resilience amplify in the presence of a strong HRM system.

Organizations that aim to utilize AI-enabled HR analytics to achieve organizational missions should also dedicate attention to its associated employee well-being outcomes.

This study enriched the literature with regard to AI-driven HRM in that it identifies the mediating role of job crafting and the moderating role of HRM system strength in the relationship between AI-enabled HR analytics and employee resilience.

Many economic, political and socio-cultural events in the 2020s have been strong headwinds for architecture, engineering and construction (AEC). Nevertheless, technological advancements (e.g. artificial intelligence (AI), big data and robotics) provide promising avenues for the development of AEC. This study aims to map the state of the literature on automation in AEC and thereby be of value not only to those researching automation and its composition of a variety of distinct technological and system classes within AEC, but also to practitioners and policymakers in shaping the future of AEC.

This review adopts scientometric methods, which have been effective in the research of large intra and interdisciplinary domains in the past decades. The full dataset consists of 1,871 articles on automation in AEC.

This overarching scientometric review offers three interdisciplinary streams of research: technological frontiers, project monitoring and applied research in AEC. To support the scientometric analysis, the authors offer a critical integrative review of the literature to proffer a multilevel, multistage framework of automation in AEC, which demonstrates an abundance of technological paradigm discussions and the inherent need for a holistic managerial approach to automation in AEC.

The authors underline employee well-being, business sustainability and social growth outcomes of automation and provide several managerial implications, such as the strategic management approach, ethical management view and human resource management perspective. In doing so, the authors seek to respond to the Sustainable Development Goals proposed by the United Nations as this becomes more prevalent for the industry and all levels of society in general.

The purpose of the research is to examine the antecedent and employee well-being outcomes of employees' perceptions of benefits schemes.

Data were collected using both paper-based and web-based questionnaires over two time points (one month apart). The sample included 281 participants in eight companies in China. Structural equation modelling was employed to investigate the relationship between Chinese traditionality, perceived benefits schemes, job involvement and emotional exhaustion.

Chinese traditionality is an antecedent of employees' perceptions of benefits schemes. Perceived benefits schemes are negatively associated with emotional exhaustion. Moreover, job involvement mediates the relationship between perceived benefits schemes and emotional exhaustion.

The data were collected in eight manufacturing companies in China, which may raise concerns about the generalisability of findings across industries, nations and cultures. Larger, more representative and cross-contextual samples are needed for future research to test the results further.

Managers should anticipate that employees with different cultural values may develop dissimilar perceptions of the same benefits schemes. Hence, managers need to communicate the benefits schemes to distinct employee groups in different ways.

Based on the conservation of resources model, this research offers theoretical insights into the mechanisms through which perceived benefits schemes influence employee health well-being. In addition, this research tests an antecedent of perceived benefits schemes.