Open Access

Purpose – This study aims to explore how Generation Z employees in South Korea perceive and experience quiet quitting (QQ) and to examine the work-context factors associated with this phenomenon. Aim(s) – The aim of this study is to clarify the impact of the phenomenon of "Quiet Quitting" among the Generation Z employees in South Korea. Design/methodology/approach – A qualitative research design was adopted. Data were collected through semi-structured, in-depth interviews with eight South Korean Generation Z employees and analysed using a seven-step grounded analysis approach. Findings – The findings suggest that, although generational differences remain a source of workplace tension, long working hours and the pursuit of work–life balance are among the most influential factors shaping participants’ attitudes toward QQ. The study further highlights the relevance of cultural factors, including hierarchical workplace structures and attitudes toward hustle culture, in understanding how QQ is experienced by South Korean Gen Z employees. Participants generally viewed QQ not as disengagement from work, but as a means of protecting themselves from excessive workloads, burnout, and perceived imbalances between effort and reward. Limitations of the study – The study is limited by its small sample size and gender imbalance. Future research should include larger and more diverse samples, conduct comparative studies across countries, and further develop conceptual and measurement frameworks for QQ in the South Korean context. Practical implications – The findings highlight the importance of addressing excessive workloads, supporting work–life balance, and reconsidering traditional workplace structures that may contribute to employee dissatisfaction and burnout among younger workers. Originality/value – Existing research on QQ has predominantly focused on Western contexts. This study contributes to the literature by providing qualitative insights into how South Korean Gen Z employees perceive and experience QQ within a distinct cultural and organisational setting.

Purpose Employee engagement is typically measured at the individual level and then aggregated to the overall organisational level, yet its implications for performance tend to become more visible at the level of work units. This paper aims to examine the current approaches to interpret work unit engagement scores. It argues for a shift away from additive approaches and towards a multiplicative perspective that better reflects how engagement translates into outcomes within work units. Design/methodology/approach The discussion draws on practitioner literature and applied HR insights. Engagement is conceptualised through two core dimensions – affective commitment and confidence in leadership direction. It explores how these individually measured perceptions interact when aggregated at the work unit level and how different aggregation logics affect interpretation. Findings While engagement is measured at the individual level, its performance implications emerge most clearly at the work unit level. Additive aggregation can mask important imbalances between engagement dimensions, whereas a multiplicative interpretation highlights that the strongest and most consistent engagement outcomes are associated with work units where both dimensions are jointly high and balanced. Imbalances between dimensions tend to constrain the translation of engagement into performance. Originality/value The study offers a refined and practical interpretation of employee engagement by linking individual-level responses to work unit performance outcomes. It provides HR practitioners with a more diagnostic lens for interpreting engagement data, enabling more effective analysis of survey results and a clearer basis for designing targeted managerial interventions.

Purpose This study sets out to clarify the conceptual, operationalization and level concerns of the practitioner-based concept of employee engagement (EE). We propose and test a multiplicative operationalization of EE as a more actionable, two-dimensional construct (affective commitment and top leadership direction) at the collective work-unit level. Further, we also substantiate the EE link to work-unit performance. Design/methodology/approach This study employs a quantitative multi-level research design. The main hypotheses are tested on a sample of 251 work units from a multinational organization, covering 21,933 employees. Findings The proposed two-dimensional EE operationalization is statistically supported at the individual and collective work-unit levels of analysis. The study reveals that the work-unit multiplicative engagement operationalization outperforms the standard additive one (i.e. lowest AIC) and has a significant positive relationship with work-unit performance. Originality/value This study is the first to apply the multiplicative EE operationalization. It uncovers that only work units with equal levels (preferably the highest scoring) of collective affective commitment and top leadership direction lead to improved (optimal) performance. This provides more specific insight for the subsequent managerial implications.

Purpose This paper aims to address the pressing need for artificial intelligence (AI)-related upskilling among human resources (HR) practitioners, who play a pivotal role in driving AI-based change, by offering a practical and structured guide for integrating AI skills and competencies into their tasks. Design/methodology/approach The guide for upskilling HR practitioners builds on established tools such as ISO 9001, job descriptions, Knowledge, Skills, Abilities and Other characteristics analysis and standardized job databases. It introduces a novel replacement of the traditional “Equipment” (E) in HR task breakdown with “AI-based Equipment” (AI-based E). The application of AI upskilling is illustrated through a practical recruitment example. This should in turn facilitate bridging the gap between existing rather abstract AI skill and competence frameworks, and concrete HR application. Findings The proposed upskilling guide enables HR practitioners to contextualize AI within familiar HR processes (i.e. starting from the known), creating an actionable path to upskilling. Three actionable strategies for identifying relevant AI-based tools are outlined: (1) conducting market research, (2) consulting AI tool databases and (3) applying AI methods in-house. This structured approach facilitates targeted training initiatives and empowers HR practitioners to navigate the AI landscape with greater confidence and autonomy. Originality/value This paper offers an actionable and application-oriented upskilling guide that leverages existing HR tools to restructure HR tasks for AI integration. This provides the basis for deriving specific AI-related training initiatives.

Im Beitrag wird ein transparentes, Large Language Model (LLM)-gestütztes Vorgehen zur Organisationsoptimierung am Beispiel einer unbemannten Roboterschicht in cyber-physischen Produktionssystemen vorgestellt. Es operationalisiert ambidextre Automatisierung durch explorative und exploitative Arbeit: Repetitive Anteile wandern zum Roboter, während Bediener geplante, auditierbare Blöcke für Beobachtung, Lernen, Standardverbesserung und kurze Erholung erhalten – bezeichnet als Explorationsrente. Eine 14-Punkte-Checkliste wird auf flexible operative Datendomänen abgebildet; das LLM fragt Retrieval-Augmented Generation (RAG)-gestützte Auswertungen ab, um zwei Stresssignale (Exploration vs. Exploitation) zu berechnen. Daraus leitet es Übergabevorschläge für die kollaborative Tagesphase, das Übergabefenster und die unbemannte Schicht ab. Entscheidungen folgen einem Human-in-the-Loop PDCA-Zyklus; die Interaktion kann lokal oder remote erfolgen. Das Konzept wird anhand messbarer Ergebnisse evaluiert: geplante menschliche Zeit, realisierte Roboter-Aktivzeit versus Plan, Unterbrechungen/Leerlauf sowie Akzeptanz. Der Beitrag präsentiert ein Szenario; empirische Schwellenwerte und Gewichtungen verbleiben als zukünftige Arbeit.