The Impact of Electronic Human Resource Management (E-HRM) Strategies on Organizational Innovation by Knowledge Repository as Mediating Role

The current study is intended to provide the analysis of the direct and indirect relationships between (E-HRM) strategies and organizational innovation through a knowledge repository as a mediation construct. From various previous studies, five (E-HRM) strategies constructs were adopted for this study. These include e-recruitment, e-selection, e-training, e-performance appraisal, and e-compensations, and used the mediating role of a knowledge repository for these constructs to improve organizational innovation. The study applied the Smart Partial Least Square (PLS) software to analyze the underlying relationships amongst the most relevant constructs between (E-HRM) strategies, knowledge repositories, and organizational innovation with a total of seven constructs. As a result of analysis of the findings from middle managers (n = 94) working for the Royal Jordanian airline. It was found that there was a strong and statistically positive significant relationship between several (E-HRM) strategies and organizational innovation by using the knowledge repository as a mediation construct.

GENDER AND CLIMATE CHANGE

The impacts of climate action is not bound by gender disparities, but still each one ofus is accountable and responsible for its management. Climate action has to be scientific expression of attributes working in synchronously with multi-stakeholders and a common framework or exchange of dialogues. If equity in action is a perspective per se, then equality in gender towards steering it forward is a gender attribute. Women, men, children and other stakeholders are all impacted, and beyond gender bias one has to work on then targets. Women face inequalities and have not been given the key role in decision making, local and indigenous knowledge repository is used for local action by women and their contribution fixes many local issues.

The Development of a MOOCs Knowledge Repository System Using a Digital Knowledge Engineering Process to Enhance Digital Entrepreneurs’ Competencies

The objectives of this research were as follows: the development of a MOOCs knowledge repository system using a digital knowledge engineering process, and a competency assessment of digital entrepreneurs engaging in a MOOCs knowledge repository system using a digital knowledge engineering process. A total of 30 people were selected as a sampling group (purposive sampling) for this study, these included Small and Medium Enterprises (SMEs) in Bangkok with expertise in Human Performance Technology (HPT), as well as the MOOCs knowledge repository system. The mean, standard deviation, percentage, and a dependent sample t-test were used in the procedure for data analysis. The research findings suggested that: (1) the overall result concerning the development of a MOOCs knowledge repository system using a digital knowledge engineering process was at the highest level (Mean = 4.89, S.D. = 0.31), and (2) the overall result regarding the competencies of digital entrepreneurs after engaging in a MOOCs knowledge repository system using a digital knowledge engineering process passed the 80% rating, according to criteria. Moreover, learners who had undertaken activities through a MOOCs knowledge repository system using a digital knowledge engineering process improved their learning outcomes with a significance level of .05 based on the research hypothesis.

Addressing the issue of undeclared work – Part I: Applying associative classification per the CRISP-DM methodology

Addressing undeclared work is a high priority in the labor field for government policymakers since it adversely affects all involved parties and results in significant losses in tax and social security contribution revenues. In the last years, the wide use of ICT in labor inspectorates and the considerable progress in data exchange have resulted in numerous databases dispersed in various units, yet these are not effectively used to increase their functions productivity. This study presents a detailed analysis of a data mining project per the CRISP-DM methodology aiming to assist the labor inspectorates in dealing with undeclared work and other labor law violations. It uses real past inspections data merged with companies characteristics and their employment details and examines the application of two Associative Classification algorithms, the CBA and CBA2, in combination with two types of datasets, a binary and a four-class. The produced models are assessed per the data mining goals and per the initial business objectives, and the research concludes proposing an innovative inspections recommendation tool proved to offer two major benefits: a mechanism for planning targeted inspections of improved efficiency and a knowledge repository for enhancing the inspectors understanding of those features linked with labor law violations.

Pedagogically-Informed Knowledge Mapping: Representing Contextualised Competences

Knowledge mapping tools in the educational technology domain currently support learners and teachers in creating and visualizing a knowledge repository, usually of subject matter content or links to such content. This research proposes a pedagogically-informed knowledge map design, where knowledge is conceptualised as a contextualized competence, and proposes a tool for creating and visualizing knowledge and recommending appropriate learning and teaching materials. An experiment is reported which found that the use of knowledge mapping and the tool significantly correlated with academic performance.

A knowledge with ontology representation for product life cycle to support eco-design activities

Purpose This study aims to develop a synthetic knowledge repository consisted of interrelated Web Ontology Language. Design/methodology/approach The ontology composes the main framework to categorize data of product life cycle with eco-design mode (PLC-EDM) and automatically infer specialists’ knowledge for data confirmation, eventually assisting the utilizations and generation of strategies toward decision-making Findings (i) utilization of a novel model with ontology mode for information reuse cross the different eco-design applications; (ii) generation of a sound platform toward life cycle evaluation; and (iii) implementation of the PLC-EDM model along the product generation process. Research limitations/implications It cannot substitute an evaluation tool of life cycle. Certainly, this model does not predict the “target and range” and/or the depiction of the “utility module” that are basic activities in life cycle assessments as characterized through the international organization for standardization regulations. Practical implications As portion of this framework, a prototype Web application is presented which is applied to produce, reuse and verify knowledge of product life cycle. Social implications By counting upon the ontology, the information conducted by the utilization is certainly semantically represented to promote the data sharing among various participants and tools. Besides, the data can be verified against possible faults by inferring over the ontology. Hence, a feasible way to a popular topic in the domain of eco-design applications extension in the industry. Originality/value The goals are: to lean on rigid modeling principles; and to promote the interoperability and diffusion of the ontology toward particular utilization demands.

A method for transforming knowledge discovery metamodel to ArchiMate models

Enterprise architecture has become an important driver to facilitate digital transformation in companies, since it allows to manage IT and business in a holistic and integrated manner by establishing connections among technology concerns and strategical/motivational ones. Enterprise architecture modelling is critical to accurately represent business and their IT assets in combination. This modelling is important when companies start to manage their enterprise architecture, but also when it is remodelled so that the enterprise architecture is realigned in a changing world. Enterprise architecture is commonly modelled by few experts in a manual way, which is error-prone and time-consuming and makes continuous realignment difficult. In contrast, other enterprise architecture modelling proposal automatically analyses some artefacts like source code, databases, services, etc. Previous automated modelling proposals focus on the analysis of individual artefacts with isolated transformations toward ArchiMate or other enterprise architecture notations and/or frameworks. We propose the usage of Knowledge Discovery Metamodel (KDM) to represent all the intermediate information retrieved from information systems’ artefacts, which is then transformed into ArchiMate models. Thus, the core contribution of this paper is the model transformation between KDM and ArchiMate metamodels. The main implication of this proposal is that ArchiMate models are automatically generated from a common knowledge repository. Thereby, the relationships between different-nature artefacts can be exploited to get more complete and accurate enterprise architecture representations.

Tracing CVE Vulnerability Information to CAPEC Attack Patterns Using Natural Language Processing Techniques

For effective vulnerability management, vulnerability and attack information must be collected quickly and efficiently. A security knowledge repository can collect such information. The Common Vulnerabilities and Exposures (CVE) provides known vulnerabilities of products, while the Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of common attributes and approaches employed by adversaries to exploit known weaknesses. Due to the fact that the information in these two repositories are not linked, identifying related CAPEC attack information from CVE vulnerability information is challenging. Currently, the related CAPEC-ID can be traced from the CVE-ID using Common Weakness Enumeration (CWE) in some but not all cases. Here, we propose a method to automatically trace the related CAPEC-IDs from CVE-ID using three similarity measures: TF–IDF, Universal Sentence Encoder (USE), and Sentence-BERT (SBERT). We prepared and used 58 CVE-IDs as test input data. Then, we tested whether we could trace CAPEC-IDs related to each of the 58 CVE-IDs. Additionally, we experimentally confirm that TF–IDF is the best similarity measure, as it traced 48 of the 58 CVE-IDs to the related CAPEC-ID.