Search Results (21)

This conceptual article introduces knowledge-centric recruitment (KCR) as a distinct dynamic capability that reframes recruitment and post-hire socialization as strategic knowledge-development activities. (1) Background: Unlike conventional vacancy-driven approaches, KCR is a proactive process through which firms deliberately access and import external organizational capabilities embodied in senior professionals—termed knowledge-hires—from rival organizations. These knowledge-hires embody tacit, socio-cognitive building blocks of capabilities developed through involvement in their prior employers’ routines and practices. (2) Methods: This article develops a micro-foundational model of KCR comprising four interrelated processes: external capability scanning and prioritization, identification of target capabilities and knowledge-hires, evaluation through the novel lens of contextual capability fit, and expectations of adaptation during onboarding. (3) Results: Contextual capability fit integrates complementary and supplementary quality with knowledge distance to enable firms to forecast both the strategic value of inbound capabilities and the hire’s expected socialization difficulty. (4) Conclusions: The primary theoretical contribution lies in advancing the learning-by-hiring literature by shifting the focus from passive knowledge diffusion to deliberate, calculative capability acquisition. By integrating insights from the knowledge-based view, person–organization fit, absorptive capacity, and strategic recruitment, the KCR model offers a coherent micro-foundational framework for transforming employee mobility into a source of sustained competitive advantage. Full article

Research on Employee Experience Management (EXM) has traditionally emphasized demographic segmentation—age, gender, or educational level—as the main differentiators in how employees perceive their career path within organizations. However, this demographic focus shows fragmented evidence and limited explanatory power regarding the mechanisms that truly shape EX. To fill this gap, this study proposes a structural and contextual reformulation of EX segmentation, arguing that employee experiences vary less depending on who they are and more depending on their position within organizational systems. Using data from 403 employees across various sectors and an 18-item EXM scale covering Reputation, Recruitment, Physical, Technical, and Cultural Experiences, we performed t-tests, ANOVAs, cluster analysis, and effect size estimates. Findings show that hierarchical position is the main predictor of EX in the dimensions of Physical, Technical, and Cultural Experience, as well as in the overall dimensions, while demographic differences—especially age and education—are negligible. Employee seniority and organisational size have a significant impact on Cultural Experience, which emphasizes how socialisation and organisational structure shape the EX. These findings reveal that EX segmentation is essentially structural, underscoring the necessity of EXM strategies that prioritize employees, particularly those without management functions, and enhance cultural integration through leader communication and onboarding procedures. Full article

The tourism industry faces increasing pressure for agile, personalized services, yet travel agencies struggle with fragmented knowledge scattered across isolated systems and legacy formats. While Large Language Models (LLMs) are widely applied in customer-facing roles, their potential to enhance internal operational efficiency remains largely underexplored. This study presents the design and evaluation of an intelligent assistant specifically for travel agency operations, built upon a Retrieval-Augmented Generation (RAG) architecture using Gemini 2.0 Flash. The system integrates heterogeneous data sources, including structured product catalogs and unstructured documentation processed via Optical Character Recognition (OCR), into a unified interface comprising work assistance, interactive training, and evaluation modules. Results demonstrate information retrieval times not greater than 45 s, ensuring its daily usability, while maintaining 95% accuracy. Furthermore, the system democratizes tacit senior expertise and accelerates new employee onboarding. This research validates RAG architectures as a powerful solution to knowledge fragmentation, shifting the strategic AI focus from customer automation to employee empowerment and operational optimization. Full article

Gen Z is an emerging talent entering the world of work with distinct characteristics, expectations, preferences, and aspirations compared to previous generations. Therefore, there is growing interest in understanding Gen Z’s entry-level experiences, particularly the psychosocial experiences of this unique generation as they transition into the world of work. Hence, the purpose of this study was to explore the psychosocial experiences of entry-level Gen Z newcomers in South African organisations. This study used a qualitative research approach, drawing on a social constructivist perspective. In-depth interviews were conducted to collect data on the lived experiences of nine Gen Z employees who had recently entered the workforce. A conventional content analysis approach was used to analyse the qualitative data. Salient psychosocial factors influencing the psychosocial experiences and professional adjustment and transition of Gen Z newcomers into the world of work were identified. This study indicated that organisations should focus on onboarding processes, supportive line managers, reasonable workloads, promoting work–life balance, providing meaningful work, supporting career management and development, creating diverse and inclusive work environments, promoting employee well-being, and providing technologically advanced and modern job tools to enhance positive psychosocial experiences of Gen Z newcomers in the workforce. This study sheds light on the psychosocial factors that promote positive psychosocial experiences among Gen Z newcomers, offering unique insights into the growing literature on the Gen Z generation. Full article

Working remotely during the pandemic changed workplace culture for millions of employees and managers, fueling a desire for more flexible work arrangements. About 75 percent of companies now support hybrid work, meaning managers often oversee workers both in and out of the office. Two large-sample surveys on these new work models reveal three management challenges: creating a consistent culture that includes both hybrid and in-office employees, strengthening connections among staff regardless of their location, and implementing effective performance management for both groups. In the management literature, existing research on each of these topics is limited. This article recommends solutions to each of these challenges based on available evidence, while exploring related topics such as new-hire and virtual onboarding, strategies to combat loneliness at work, proximity bias, and employee monitoring. The article concludes with a set of research questions that emerge from the topics reviewed. Full article

Drawing on conservation of resources theory, this study examines how existing employees’ calling orientation shapes their responses to newcomers’ job crafting. Using a scenario-based experimental vignette design with 149 full-time employees who work in teams, this study tested how two distinct forms of newcomers’ job crafting—approach- and avoidance-oriented—affect veterans’ helping intention toward newcomers. The results show that approach-oriented job crafting increases, whereas avoidance-oriented job crafting decreases, helping intentions. More importantly, both effects were mitigated by observers’ calling orientation: experienced employees with a strong calling orientation consistently displayed higher helping intentions and were less reactive to the degree of newcomers’ crafting behaviors. This study extends interpersonal job crafting research to newcomer–veteran relationships rather than the peer or leader–subordinate contexts that dominate prior work. The findings deepen the understanding of how proactive newcomers are received in organizations and suggest that integrating employees’ work orientation profiles into mentoring and onboarding systems can enhance welcoming practices in the workplace. Full article

Current federated identity management systems lack contextual awareness of workflows across independent systems, creating security gaps and workflow integrity challenges. This article details the design and implementation of Alohomora, a distributed workflow-aware authentication system that maintains cross-system workflow context through path-bound tokens. Alohomora complements existing identity providers such as OAuth and SAML by adding workflow orchestration capabilities while leveraging standard authentication protocols for initial user verification. The system introduces workflow graphs as a formal model for representing dependencies between functions across heterogeneous systems and employs a distributed caching architecture with collaboration groups for scalable session management. In a typical deployment scenario, an employee onboarding workflow across human resources services, account provisioning, and benefits systems forms a trust group where Alohomora enforces ordered step execution, validates prerequisite completion at each transition, and generates cryptographic completion assertions upon workflow finalization. Extensive performance evaluation under concurrent user requests demonstrates polynomial performance characteristics with superior scalability compared to centralized OAuth introspection. The results show that Alohomora maintains high throughput under heavy load while providing strong, secure access control through workflow path binding and distributed trust orchestration. The prototype implementation is available as open source. Full article

Employee onboarding is a key process in workforce integration but is manual, time-consuming, and departmental. This paper presents OnboardGPT v1.0, an intelligent, scalable conversational AI platform to meet this task with automated and personalized onboarding experience through lightweight neural components. The platform uses a feedforward intent classification model, dense semantic retrieval through cosine similarity, and personalization aware of user profiles to deliver context-sensitive and relevant output. A 500-question proprietary dataset about onboarding and annotated answers was constructed to simulate real enterprise conversations from various roles and departments. The platform was launched with a Flask-based web interface that was not third-party API-dependent and enabled multi-turn dialogue, knowledge base searching, and role-aware task instruction. Experimental evaluation on performance indicators such as task success rate, intent classification accuracy, BLEU score, and user satisfaction in simulation demonstrates the system to be effective in offering coherent and actionable onboarding support. The contribution of this work includes a modular, explainable, and deployable AI pipeline suitable for onboarding automation at the enterprise level and lays the foundation for future extensions that include multilingual support, inclusion of long-term memory, and backend system interoperability. Full article

Aim: This review aims to validate the Experience-Based Onboarding Model (EBOM) and refine it into an improved adaptive onboarding model, OnMod. Methods: In this review, autoethnography is combined with a Multivocal Literature Review (MLR) that combines white and gray literature sources. Evidence is mapped to entities and semantic relations and assessed using predefined decision rules. Main findings: The validation of the model confirms the core EBOM entities and semantic relations. It also introduces several new or renamed entities or semantic relations that close the feedback loop and yield the refined OnMod model. Implications: The theoretical contribution is reflected in the application of autoethnography in combination with the MLR, where it represents a good basis for the development of an onboarding model. In industrial practice, the presented OnMod model can be used by mentors and managers as a guide for improving operational and daily activities, as well as for the development of onboarding strategies in IT and software companies. Full article

This study explores the relationship between digital human resource management (HRM) practices, organizational culture, and employees’ perceived digital competence within Greek organizations. While digitalization has become a central priority in human resource management (HRM), there is still limited understanding of how cultural context shapes the effectiveness of digital HR interventions. Using a quantitative approach, data were collected via an online questionnaire from 257 employees across various sectors. The research employed the method of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (MGA) to examine the structural relationships between digital HRM practices—such as e-learning, onboarding, and performance management—and digital competence, taking into account different organizational culture profiles. The results show that digital HRM practices have a positive, but modest, impact on employees’ digital skills, with e-learning emerging as the most influential factor. Importantly, the effect of HRM practices varies significantly according to the cultural environment: supportive and innovative cultures foster stronger development of digital competence compared to hierarchical settings. The findings underline the necessity for organizations to adapt digital HR strategies to their specific cultural context and not to rely solely on technological solutions. This research contributes to the growing literature by demonstrating the interplay between technology and culture in shaping employees’ digital capabilities and suggests that a balanced focus on both is essential for successful digital transformation. Full article

In an increasingly competitive labour market, talent retention has become a critical challenge for organisations. In this context, the onboarding process assumes a strategic role, as it not only facilitates employee integration but also strengthens ties with peers and direct supervisors, enhances engagement levels, and consequently fosters retention within the organisation. This study involved 280 employees. A quantitative, cross-sectional approach was adopted to validate the proposed hypotheses. Data were collected from a convenience sample of employees through a questionnaire survey. The results revealed that the onboarding process had a significantly positive impact on talent retention. Furthermore, both interpersonal connections and engagement were found to mediate the relationship between the onboarding process and talent retention, confirming a sequential mediation effect. These findings reinforce that an effective onboarding process strengthens interpersonal relationships, boosts engagement, and thereby enhances talent retention. Full article

Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations. Full article

By integrating the concept of sustainable human resource management and Rock’s neuroscience-based SCARF (status, certainty, autonomy, relatedness, and fairness) model, the paper aims to reveal the role of sustainable HRM practices in shaping a positive psychosocial experience of employees. Through this process, qualitative data were collected using semi-structured interviews with 50 STEM (science, technology, engineering, and mathematics) workers in Lithuania. The research revealed the key characteristics of sustainable HRM practices in terms of performance management, employee relations, rewards, employee development, career management, onboarding, and dismissal that shape positive experiences of STEM workers. Moreover, harmful HRM practice characteristics were identified that need to be eliminated to achieve sustainable human resource management. The results of the study allow organizations to better understand the role of HRM practices in enhancing positive psychosocial experiences of STEM employees through strengthening the domains of the SCARF model. Based on the insights obtained, organizations could better focus their efforts on the development of sustainable HRM practices that not only increase employee engagement but also contribute to their long-term professional sustainability. Full article

This paper presents a numerical sketch-based methodology to achieve optimal product design solutions, bridging the gap between initial conceptual sketches and advanced engineering analyses. The proposed approach enables the transformation of simple hand-drawn sketches into digital models suitable for complex computational simulations and design optimization. Using computer vision algorithms, sketches are processed to generate digital design components that serve as inputs for Finite Element Analysis (FEA). In order to further enhance the overall design process, topology optimization (TO) is also performed, iteratively refining the geometry to achieve optimal material distribution for improved structural performance. Additionally, Adaptive Mesh Refinement (AMR) techniques are applied to ensure computational efficiency and accuracy by dynamically refining the mesh in regions of high complexity or stress concentration. The synergy of sketch-based modeling, FEA, TO, and AMR demonstrates significant potential in reducing design cycles while maintaining high-performance standards. Finally, it should be noted that the proposed pipeline consists of a fully automated procedure, hence it could reduce the learning curve for the designers, enabling companies to onboard employees faster and integrate advanced design techniques into their workflows without extensive training. The above-mentioned modules render the introduced approach particularly suitable for applications in product design development that can be utilized in several industries like mechanical, manufacturing, and furniture. Full article

HR decision-making is changing as a result of artificial intelligence (AI), especially in the areas of hiring, onboarding, and retention. This study examines the use of AI tools throughout the lifecycle of an employee, emphasizing how they enhance the effectiveness, customization, and scalability of HR procedures. These solutions streamline employee setup, learning, and documentation. They range from AI-driven applicant tracking systems (ATSs) for applicant selection to AI-powered platforms for automated onboarding and individualized training. Predictive analytics also helps retention and performance monitoring plans, which lowers turnover, but issues such as bias, data privacy, and ethical problems must be carefully considered. This paper addresses the limitations and future directions of AI while examining its disruptive potential in HR. Full article