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When a firm implements certain HR practices, different employees attribute different motives and intentions to the firm with regard to those HR practices. Research on HR attributions has made progress toward understanding the relationship between HR practices and employee outcomes from a process perspective. However, this research is still fragmented and lacks a systematic typology of the different types of HR attributions and a compelling organizing research framework. Furthermore, a number of research gaps and opportunities have emerged regarding the nomological net of employee HR attributions. To address the gaps and capitalize on the opportunities, the authors propose an overarching theory-driven multi-level framework that guides the choice of the antecedents and outcomes of employee HR attributions and explains their relationships along with both mediating and moderating mechanisms. Drawing on signaling theory embedded in the proposed framework, the authors identify and categorize various antecedents of employee HR attributions to explain their relationships. The authors also use several additional theories such as social exchange and the job demands–resources model included in their review to identify and categorize various outcomes of employee HR attributions across levels of analysis (i.e., individual, collective [team/group/unit], organization) and explain their relationships. In addition, the proposed framework explains how individual-level employee HR attributions emerge at the collective level and influence collective processes and outcomes. The authors end their review by pinpointing future research needs and discussing related future research directions.

Our study examined whether work engagement follows a predictable pattern over the course of the work week and the role of personality traits in shaping this pattern.

We examined these questions with 131 employees from Canada and the United States who provided daily ratings of work engagement over the course of 10 work days.

Multilevel modeling revealed that employee engagement followed an inverted U-shaped curvilinear pattern from Monday to Friday, peaking midweek. Neuroticism moderated the change pattern of engagement across the work week, such that individuals with higher levels of neuroticism experienced lower and less stable levels of work engagement throughout the work week compared with individuals with lower levels of neuroticism. However, extroversion and conscientiousness did not moderate the change pattern of employee engagement.

These results provide insight into the entrainment of work to the work week and how this entrainment is further affected by the personality trait neuroticism.

Understanding the weekly pattern of work engagement will help leaders’ time work assignments, interventions, and training sessions to keep the levels of employee engagement high.

Our study revealed novel predictors of within-person engagement: weekly entrainment and neuroticism.

Introduction: The Covid-19 pandemic wreaked havoc on the organisations in the form of increased job demands which manifested through increased workload, time pressure, etc. Similarly, stress and burnout engulfed the employees. Remote work became the new normal post-pandemic. Remote workers require more engagement. This has brought Artificial Intelligence (AI) to the forefront for engaging employees in the new normal.

Purpose: With limited studies on AI-enabled employee engagement in the new normal, this study investigates and proposes a conceptual framework of employee engagement in the context of AI and its impact on organisations.

Methodology: A systematic review and meta-synthesis method is undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes.

Findings: Since many organisations are turning to remote work post-pandemic and remote work requires more engagement, organisations are investing in AI to boost employee engagement in the new normal. Several antecedents of employee engagement such as quality of work life, diversity and inclusion, and communication are facilitated by AI. AI helps enhance the quality of work life by playing a major role in providing fair compensation, safe and healthy working conditions, immediate opportunity to use and develop human capacities, continued growth and security, work and total life space, and social relevance of work life. This has led to positive organisational outcomes like increased productivity, employee well-being, and decreased attrition rate. Furthermore, AI helps in measuring employee engagement. The various tools of AI, such as wearable technology, digital biomarker, neural network, data mining, data analytics, machine learning (ML), natural language processing (NLP), etc., have gone a long way in engaging employees in the new normal.

For decades organizational citizenship behavior (OCB) has been of interest to scholars and practitioners alike, generating a significant amount of research exploring the concept of what citizenship behavior is, and its antecedents, correlates, and consequences. While these behaviors have been and will continue to be valuable, there are changes in the workplace that have the potential to alter what types of OCBs will remain important for organizations in the future, as well as what types of opportunities for OCB exist for employees. In this chapter we consider the influence of 10 workplace trends related to human resource management that have the potential to influence both what types of citizenship behaviors employees engage in and how often they may engage in them. We build on these 10 trends that others have identified as having the potential to shape the workplace of the future, which include labor shortages, globalization, immigration, knowledge-based workers, increase use of technology, gig work, diversity, changing work values, the skills gap, and employer brands. Based on these 10 trends, we develop propositions about how each trend may impact OCB. We consider not only how these trends will influence the types of citizenship and opportunities for citizenship that employees can engage in, but also how they may shape the experiences of others related to OCB, including organizations and managers.

This article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions.

The objective of this chapter is to identify the key characteristics of Global Services businesses that will thrive and achieve success in the future. These factors are integrated into three main pillars, which we refer to as the Triple-Win. The first and most obvious pillar is technology as a tool. The second pillar is the design and sustainability of the business model, without which the previous factor would be merely a cost and not an investment. And last but not the least, there is the purpose which gives meaning to the proposal, focusing on the human being and their environment. The DIDPAGA business model sits at the intersection of these three elements.

The COVID-19 pandemic ushered in multiple challenges for employees, which led to employee turnover, disengagement at work, employees’ mental health issues, etc. The study tries to elucidate how artificial intelligence (AI) herald great promise in human resource management in decreasing cost, attrition level and enhancing productivity. Considering the dearth of studies on recent trends in human resource management (HRM) in the context of AI, the study elucidates the role of AI in facilitating seamless onboarding, diversity and inclusion (D&I), work engagement, emotional intelligence and employees’ mental health. Thus, a conceptual model of recent trends in HRM in the context of AI and its organisational outcomes is proposed. A systematic review and meta-synthesis method are undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes. The findings of the study suggest that using natural language processing (NLP) and robots has eased the onboarding process. D&I is promoted using data analytics, big data, machine learning, predictive analysis and NLP. Furthermore, NLP and data analytics have proved to be highly effective in engaging employees. Emotional Intelligence is applied through AI simulation and intelligent robots. On the other hand, chatbots, employee pulse surveys, wearable technology, and intelligent robots have paved way for employees’ mental health. The study also reveals that using AI in HRM leads to enhanced organisational performance, reduced cost and decreased intention to quit the organisation. Thus, AI in HRM provides a competitive edge to organisations by enhancing the performance of the employees.

This chapter provides an updated review of research examining the relationship between occupational stressors and job performance. We begin by presenting an eight-category taxonomy of workplace stressors and we then review theories that explain the relationships between workplace stressors and job performance. The subsequent literature review is divided into two sections. In the first section, we present a summary of Jex's (1998) review of research on the job stress–job performance relationship. In the second section, we provide an updated review of the literature, which includes studies that have been published since 1998. In this review, we evaluate how well the contemporary research has dealt with weaknesses and limitations previously identified in the literature, we identify and evaluate current trends, and we offer recommendations and directions for future research.

Introduction: Human resource management (HRM) is going through a transformation phase due to the pandemic. The COVID-19 crisis compelled the employees to work virtually. To mitigate the effects of COVID-19, several organisations heavily invested in artificial intelligence (AI) in the realm of HRM.

Purpose: With limited studies on the paradigm shift in HRM post-pandemic and the role of AI, the study investigates and proposes a conceptual framework for the paradigm shift in HRM practices post-COVID-19 pandemic and the significance of AI. Furthermore, the study investigates the outcomes of the use of AI in HRM for organisations and employees.

Methodology: A comprehensive review of the literature based on the guidelines of Tranfield, Denyer, and Smart (2003) and Crossan and Apaydin (2010) has been followed. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes involved.

Findings: COVID-19-related economic disruption has led to a paradigm shift in HRM practices. AI-enabled HRM practices are now centred around remote and contingent workforce management, mindfulness, social capital, increasing employee engagement, reskilling and upskilling towards new competencies, etc. AI is making remote work seamless through smooth recruitment and selection process, onboarding, career and development, tracking and managing the performance, facilitating learning, and talent management. Post-pandemic, AI-powered tools based on data mining (DM), predictive analytics, big data analytics, natural language processing (NLP), intelligent robots, machine learning (ML), virtual (VR)/augmented reality (AR), etc., have paved the way for managing the HRM practices effectively, thereby leading to enhanced organisational performance, employee well-being, automation, and reduced cost.

Digital transformation is a much prevalent process being practiced in the different spheres of the business sectors. This transformational framework has been made possible by the extensive use of digital technologies. Nevertheless, companies are serious regarding the digital transformation process but hesitate in their stock of workers possessing digital abilities. The building up of dexterous teams is of paramount importance in achieving goals. In the research, there has been an examination of the techniques and methods used by select companies to encourage and develop their employees to become attuned with the digital transformation processes implemented. The author has also conducted an exploratory study to identify the sophisticated techniques used by most new companies on their workforce to upgrade their digital awareness and capabilities. Furthermore, in doing so, a model has been developed to sustain, evolve, and ultimately to transform the employees in the digital era.