This research tackles contemporary human resource management challenges using advanced analytics methodologies. Initially, workforce dynamics are analysed through clustering to segment employees based on attributes. Among several algorithms evaluated, including K-means, agglomerative clustering, spectral clustering, and Gaussian mixture models, K-means proves most effective, with a Silhouette Score of 0.874156 and a Davies-Bouldin score of 1.285476. The study then predicts future skill requirements using deep learning models, focusing on the dense neural network. The dense NN emerges as the top predictive model, with the lowest mean squared error of 4478.58, the lowest mean absolute error of 47.56, and the highest R2 score of 0.94. Additionally, feature importance analysis highlights the dense NN's ability to capture intricate relationships, aiding HR practitioners in understanding key predictive factors. This research equips HR professionals with critical insights for proactive talent management and workforce planning.
Nowadays, organisations are increasingly aware of the importance of optimising the use of their knowledge resources and adopting a quality management model based on a process-centric approach. This approach requires a multidisciplinary approach that integrates the domains of knowledge management, business process management, and process mining. Thus, to enhance their performance and increase their responsiveness, organisations must identify, manage, and monitor all business processes (BPs) that may leverage crucial knowledge. It is imperative to implement a computerised system automating business processes to achieve these goals. In this context, we propose a new method for predicting the execution times of business processes, named BPETPM, based on the CRISP-DM approach. We employed machine learning techniques to exploit the execution data of a workflow engine. To demonstrate the relevance of this method, we developed an intelligent system for predicting BP execution times, called iBPMS4PET.
This study reveals the importance of tacit knowledge and suggests favouring teacher-student interactions in online synchronous courses in the case of higher education. Our empirical study surveyed 171 students who had been learning in online-synchronous mode in higher education in France since the COVID-19 pandemic. They were from six French higher schools and had backgrounds in either computer science or management science. We found that 57% of respondents preferred face-to-face learning versus 13% who preferred online learning. For respondents who preferred face-to-face, the syntactic analysis shows that this format allowed them to interact more easily with the teacher and their classmates.
This academic work delves into how quality management practices (QMP) in supply chains augment firm performance through knowledge sharing behaviour (KSB) and quality management capacity (QMC). The data was garnered from 276 small and medium-sized enterprises (SMEs) operating in high-pollution industries, including paper, fashion, and food manufacturing, across the USA. Draw on structural equation modelling, QMP was disclosed to significantly amends innovative performance (through KSB and QMC) and operational performance (through KSB). Nevertheless, the path conjoining QMP to operational performance via QMC was statistically inconsequential, exposing QMC's stronger function in driving innovations over routine operations. The outcomes deliver empirical evidence for SMEs in polluting sectors to deliberately allocate resources: KSB advances the entire performance elements, while QMC investments generate greater returns for innovations. These novel comprehensions help manufacturers balance operational efficiency with sustainability-driven innovations.
The study aims to understand and analyse the antecedents of knowledge hiding behaviour and their impact on the formation of this behaviour through importance-performance map analysis within the Iraqi context, specifically at the Northern Technical University. The descriptive-analytical approach was adopted by reviewing relevant literature to identify the most commonly agreed-upon antecedents, which were then examined based on individual, interpersonal, and organisational antecedents. The research population consisted of 1,800 individuals, from which a non-random sample of 316 participants was selected. Data were collected through a questionnaire developed based on a comprehensive scientific review. The study found that knowledge hiding behaviour at the Northern Technical University primarily stems from organisational antecedents, which contribute to the formation of an unhealthy work environment. This, in turn, leads to the development of individual and interpersonal antecedents, all of which collectively foster KHB. Therefore, all antecedents play a significant role in shaping KHB in the Iraqi context. This study represents an emerging research direction in the Arab world - particularly in Iraq - aimed at exploring the antecedents of knowledge hiding in organisations.
This study aims to explore the influence of a competitive work environment on employees' openness to knowledge-sharing. It specifically examines the mediating role of co-workers' desire to learn and the moderating role of job security on knowledge-sharing behaviour. A conceptual model is developed based on existing literature drawing on social relations theory to analyse these relationships. Data were collected from 237 employees across eight banks and analysed using structural equation modelling. The study finds that co-worker desire to learn fully mediates the relationship between competitive work environment and openness to knowledge sharing. Additionally, the results indicate that incentives for knowledge sharing moderate this relationship. These results provide insight for organisations aiming to develop a culture of knowledge sharing in a highly competitive environment and lay a foundation for exploring further moderators and mediators.
Ontological Knowledge Bases (OKBs) play a vital role in structuring domain-specific knowledge and serve as a foundation for effective knowledge management systems (KMS). However, their traditional manual development poses significant challenges related to scalability, consistency, and adaptability. Recent advancements in generative AI, particularly large language models (LLMs), offer promising solutions for automating and enhancing OKB development. This paper introduces a structured, iterative methodology leveraging LLMs to optimise knowledge acquisition, automate ontology artefact generation, and enable continuous refinement cycles. We demonstrate this approach through a detailed case study focused on developing a user context profile ontology within the vehicle sales domain. Key contributions include significantly accelerated ontology construction processes, improved ontological consistency, effective bias mitigation, and enhanced transparency in the ontology engineering process. Our findings highlight the transformative potential of integrating LLMs into ontology development, notably improving scalability, integration capabilities, and overall efficiency in knowledge management systems.
In today's context, rethinking knowledge management (KM) from the artificial intelligence (AI) perspective is necessary for organisations to gain a competitive advantage by adopting different innovative techniques and strategies. This conceptual study will discuss the dynamic interplay between KM and AI in modern organisational structures and processes. It explores how AI transforms traditional KM practices, focusing on AI's ability to automate knowledge discovery, enhance decision-making, and foster innovation and collaboration. The results show that integrating AI into KM is crucial to organisational efficiency, productivity, and competitiveness. It further highlights the challenges and opportunities of this integration, emphasising the importance of ethical considerations, data privacy, and user trust. Further, we examined the different case studies and real-world examples of organisations (IBM Watson, Microsoft SharePoint, SAP, Deloitte, and Siemens) that successfully implemented AI with KM systems. Finally, it proposes strategies for organisations to manage AI technologies within their KM frameworks.
This research investigates the possibility of utilising the implicit and explicit knowledge of cybersecurity professionals in order to help small and medium-sized businesses (SMEs) in assessing the level of security that their information and knowledge systems possess. A dominance-based rough set approach serves as the foundation for the proposed strategy, which consists of two primary stages. In order to generate three ordered decision classes, the first phase requires the construction of a set of criteria and preference models, which are guided by seasoned security specialists. Validation of this preference model is performed with the help of test data during the second step. Forty-three small and medium-sized enterprises (SMEs) and 15 cybersecurity specialists participated in the testing of the method. By taking this method, firm managers are able to better anticipate cybersecurity risks, provide a comprehensive review of information system security, and reduce the likelihood of cyberattacks.
This paper investigates the extent to which organisational performance (OP) is shaped by human-machine interaction (HMI), organisational communication culture (OCC), and knowledge sharing (KS) within Indonesian organisations. With rapid digital transformations, knowledge on how socio-technical drivers interact is crucial. Utilising a multi-method approach that blends structural equation modelling and Bayesian networks, 420 workers from medium and large organisations were polled. Findings indicate: 1) HMI and KS substantially enhance OP; 2) OCC has a direct impact on OP and moderates the influences of HMI and KS; 3) Bayesian analysis identifies OCC as the most substantial predictor of high OP; 4) the amalgamated framework explains 67% of OP variance. The work furthers the extant literature by utilising cutting-edge statistical and machine learning procedures in a developing economy context, highlighting the pivotal role of communication culture in facilitating maximum benefits from digital teamwork and knowledge flow.
This study investigates how local governments foster public sector innovation by applying the socialisation, externalisation, combination, and internalisation (SECI) model. A qualitative case study methodology was employed, involving 33 semi-structured interviews with officials from five local governments: Sumedang, Banyuwangi, Deli Serdang, Aceh Jaya, and Aceh Tenggara. Thematic analysis and NVivo software were used to explore knowledge management (KM) processes. The findings indicate that the effective implementation of models through coordination, documentation, data integration, and training enhances organisational capacity and innovation. However, barriers, such as budget constraints, organisational cultures, and inadequate policies, can impede optimal outcomes. The study concludes that integrating the model into local government practices can foster sustainable innovation, particularly when supported by leadership commitment, a collaborative culture, and robust digital infrastructure. Theoretically, this research contributes to the KM literature and offers practical guidance for transforming local governments into adaptive learning organisations.
