The increasing availability of generative AI tools raises the question of whether they can meaningfully support leadership communication. One use case discussed in the literature is AI's potential to enhance empathetic communication. Yet little is known about whether leaders are willing to use AI's support or how observers interpret such communication. This research examines the topic from two complementary perspectives using two vignette-based experiments with working adults in Germany. In Study 1, participants in the leadership scenario received advice framed as coming from an AI system or a human communication specialist before revising a previously written feedback email. We compared empathy levels of emails before and after giving advice to determine whether participants were willing to use AI advice. The likelihood of revising the email in response to the advice did not differ depending on the human vs. AI condition, nor did we find differences in terms of improvement of empathy. Study 2 investigated how observers interpreted leadership communication with varying degrees of AI involvement (no AI, low AI, and high AI). High AI use (generating the whole message with AI) led to lower evaluations of empathy, credibility, and feedback quality compared to the no-AI condition, whereas low AI use (using AI to fix wording) did not result in negative reactions. Tentative evidence suggests that AI literacy may mitigate the negative effect of high AI involvement on perceived empathy, but it did not moderate the effects on credibility or quality. Together, the findings show that while individuals were open to integrating AI advice when communicating in a leadership scenario, high AI involvement can negatively affect how observers interpret their messages.

The concept of human energy (HE) is embedded in various theories and has been widely studied in organizational psychology research. This meta-analysis aims to examine a nomological network of individual HE at work and to investigate whether relationships between conceptualizations of HE, such as vigor and vitality, assessed with different measures, show comparable patterns of associations with work-related constructs. Moreover, we compared this nomological network to those of engagement and thriving, two related constructs that include HE as part of their multidimensional conceptualizations, to gain insights into their conceptual distinctiveness and empirical overlap. Analyzing 198 studies, the findings revealed that HE positively and moderately to strongly correlates with positively connoted construct categories (e.g., job and organizational resources, job attitudes, performance) and negatively correlates with negatively connoted construct categories (i.e., negative well-being, negative work-related intentions and behaviors). Relationships with job stressors varied, suggesting the need to distinguish between stressor types. The type of measurement influenced the relationships between HE and most construct categories, with small to moderate effect size differences. Our analysis revealed notable alignment between the HE, engagement, and thriving nomological networks. We conclude that individual HE at work, as a parsimonious, mostly unidimensional construct, offers advantages over comparable multidimensional constructs. We discuss implications for future research and encourage scholars studying HE at work to carefully consider its conceptualization and theoretical foundation, and to transparently report its measurement to enhance construct clarity.

Purpose This study aims to explore the current state of generative artificial intelligence (genAI) in the workplace and discuss a potential digital divide in relation to genAI. Design/methodology/approach Using a quantitative approach, we study career-relevant predictors – family socio-economic status, education and work characteristics – and their relationship with different indicators of digital divide – access, genAI use, attitude toward AI and perceived AI literacy. To test our hypothesis, we used logistic and linear regression analyses. Additionally, latent profile analysis was conducted to identify patterns regarding work characteristics within the sample. Findings Among the 1,341 participants, 326 individuals were genAI users. Our results show that higher family socio-economic status, education and enriched and demanding work can be linked to a more positive attitude toward AI and higher perceived AI literacy. In the case of access and frequency of use, the results were mixed. Originality/value Our findings offer a novel contribution by examining a potentially upcoming digital divide in the case of genAI. We focus on how the career adaptation of the workforce might develop in the age of genAI. Importantly, we highlight that not all individuals may have an equal opportunity to adapt to genAI, which could hinder their future career development and reinforce patterns of inequality. Future research should address how to promote inclusivity and consider individual differences in adapting to genAI.

As digital tools are used more and more and work is done from home, working conditions are changing. This article analyzes these changes using eight cases and identifies similarities and differences. Predicting learning, well-being, and work performance makes it possible to assess undesirable developments in work design. Practical relevance : By paying attention to potential undesirable developments, countermeasures can be taken, such as improving communication and social support for distributed work, or adapting the design of AI systems.

In the present study, we introduce the concept of availability ambiguity and propose that it extends our understanding of the consequences of availability expectations after hours beyond the absolute level perceived by employees. Thus, we investigated how the level and ambiguity of supervisors' availability expectations contribute to ICT communication satisfaction, detachment, work-home interference, and exhaustion. Furthermore, we test the effectiveness of a training for supervisors aimed at encouraging them to be transparent about their availability expectations by making explicit agreements with their team. In cross-sectional Study 1, data from 235 individuals showed that availability ambiguity predicted detachment and work-home interference beyond the effect of availability expectations. This finding underscores the need for clear agreements, which was addressed in an intervention tested in the two-wave Study 2. Results from 62 subordinates at T1 and 33 at T2 belonging to 17 different supervisors who participated in the training indicated an increase in explicit agreements and a decrease in availability ambiguity, but no decrease in levels of availability expectations or emotional exhaustion and no increase in ICT communication satisfaction, detachment, or work-life balance. Taken together, our studies show that the ambiguity of availability expectations is a unique stressor that needs to be and can be targeted.

Abstract: With an increasing use of work-related technologies after hours and mobile working, boundaries between work and personal life domains blur more and more, impairing recovery. Qualitative studies have shown that individuals use various boundary work tactics to actively manage their work–nonwork boundaries. However, it remains largely unknown how the use of such tactics contributes to recovery. This research differentiates types of availability-related boundary work tactics and organizes them according to their underlying motives: preventive, restrictive, and rejecting tactics. The results of a cross-sectional study ( N = 249) and a validation study ( N = 175) support the proposed motive-oriented structure of tactics and show differential prediction of psychological detachment and relaxation. Implications for practice and future research are discussed.

IntroductionHigh email load has been associated with impaired well-being because emails impose specific demands, disturb the workflow, and thereby overtax individuals’ action regulation toward prioritized goals. However, the causes and well-being-related consequences of email load are not yet well understood, as previous studies have neglected the interaction type and function of emails as well as co-occurring stressors as antecedents of high email load and have relied predominantly on cross-sectional designs.MethodsIn two studies, we aimed to clarify the nature of email load through the lens of action regulation theory. The first study, a two-wave investigation with a fortnightly interval, examined the lagged relationships among email load, work stressors, strain, and affective well-being. The sample included 444 individuals across various occupations and organizations, with 196 of them working from home or remotely at least part of the time. In the second cross-sectional study, we surveyed 257 individuals using a convenience sampling approach, 108 of whom worked from home or remotely at least partially. This study focused on evaluating how different email classes—distinguished by email interaction type (received vs. processed) and email function (communication vs. task)—serve as predictors of high email load.ResultsIn Study 1, we found a positive lagged effect of high email load on strain, even when controlling for the co-occurring stressors time pressure and work interruptions. In addition, lagged effects of email load on time pressure and interruptions were identified, while no evidence was found for the reverse direction. The results of Study 2 suggest that only the number of communication-related emails received, but not the number of task-related emails received, or the number of all emails processed contribute to high email load.ConclusionFindings suggest that email load can be considered a unique stressor and that different classes of email need to be distinguished to understand its nature. Clarifying the sources of email load can help develop effective strategies to address it.

In our mobile working world, boundaries between work and non-work domains are more and more blurred, which can impair professionals' recovery and well-being. Consequently, managing work-non-work boundaries represents an important challenge for professionals. Research suggests that boundary work tactics conveyed in boundary management interventions may promote recovery and well-being. However, the efficacy of boundary work tactics is largely unknown, as well as theoretical mechanisms that may explain the effectiveness of boundary management interventions in regard of both training design and training transfer. Building on the social cognitive theory of self-regulation, we develop a web-based boundary management training. Based on the integrated training transfer and effectiveness model, we evaluate its effects on the three levels of training effectiveness: (1) perceived learning, (2) cognitions and behaviours, with boundary control and boundary creation as indicators, and (3) recovery and well-being. Results of our randomized controlled intervention study show several expected changes in boundary creation, suggesting that drawing on the social cognitive theory of self-regulation for training design can result in effective behaviour change. Intervention effects on recovery and well-being are more ambiguous, hinting at the power but likewise potential limitations of boundary creation.

In times of digitization and mobile work, the buzzword virtual leadership has generated momentum. Our contribution looks at the shift in communication in a digitized world of work, particularly in daily interactions between managers and employees. This shift requires leaders to effectively use digital technologies, with limited use of non-verbal behaviors compared to face-to-face communication. At the same time, the development of artificial intelligence (AI) is advancing, offering the potential to augment leadership in challenging areas. Our focus lies on the application of generative AI in communication between managers and employees. We shed light on the opportunities and challenges of AI-mediated leadership communication and examine how relationships between leaders and employees evolve in the age of AI. Drawing on theories of business psychology, we provide an overview of the topic, present results from our recent study, and discuss implications for further research.