Business education has been currently challenged by the fast introduction of online learning platforms for students enrolled in higher education who had been previously used only face-to-face interaction, raising questions about the sustainability of online education. This new learning environment creates a different path for students managing their knowledge, who, due to the influence of online experiences, could develop different skills with different outcomes for their chances of employment. This study analyses knowledge management of business students in an online education setting to discover its influence on students’ perception of both their general employment chances, and specifically in online businesses. To conduct the study, we opted for a quantitative research design based on a questionnaire applied between November and December of 2020, which resulted in valid responses from 256 Bachelor’s and Master’s Degree students. In line with the research hypotheses, correlation, reliability tests and logistic regression were used to perform data analysis. The results clearly indicate that students’ perception of their employment chances depend on the increasing score of knowledge acquisition, knowledge revision, conceptual change and knowledge application, independently and under the action of control variables. Additionally, knowledge application proved to be an important determinant for students’ perception of their employment chances in online businesses.

In organizations, the knowledge creation remained an exclusive attribute of humans until the emergence of artificial intelligence. The role of information systems in organizations expanded continuously, including recording, organizing, processing, storage, dissemination and making available the information, until the revolutionary emergence of artificial intelligence. The role of knowledge creation is no longer exclusive to humans. Artificial intelligence becomes a partner entity of employees in knowledge creation processes in organizations. This article investigates how artificial intelligence can be incorporated in knowledge creation processes. Starting from the study of scientific literature, this article presents concepts and taxonomies associated with the use of artificial intelligence in the processes of knowledge creation in organizations. The study is a first step in a larger research, which aims to identify the main consequences, on employees and organizations, of the incorporation of artificial intelligence in the processes of knowledge creation.

The aim of this study is to describe the relations between low demand, cash flow problems, employee dismissals, and temporary leaves experienced by Romanian companies during the economic lockdown in the first two months of the COVID-19 pandemic outbreak in Romania (16 March–16 May 2020). For this purpose, we conducted a quantitative study using descriptive, correlation, and regression analyses applied to data collected from company decision-makers. Our results show that demand for products/services and cash flow in companies has decreased significantly, the workforce being affected by dismissals and temporary leaves/furloughing. Additionally, the average linear revenue of companies has been decreasing due to insufficient cash flow, higher absenteeism of workers, customers having been affected, and the demand going down. Therefore, employee dismissals, employee temporary leaves (furlough), and dismissal intentions have been positively correlated with insufficient cash flow in companies and an extremely low demand.

This study aims to put forward measuring scales for two central concepts – vulnerabilities and knowledge risks – and identify the factors composing the two constructs. The research presented in this paper is part of a larger project investigating knowledge vulnerabilities and risks appearing in knowledge processes of organizations. In this sense, a new ontology developed by Brătianu et al. (2022) was used to design an instrument for measuring risks and vulnerabilities related to knowledge processes in organizations. Organizational knowledge dynamics perspective (Brătianu et al., 2022) was used to group knowledge risks typologies into the following clusters: knowledge creation, knowledge acquisition, knowledge loss, knowledge sharing, knowledge use, emotional knowledge dynamics, spiritual knowledge dynamics. Examples of knowledge risks belonging to such clusters may include a knowledge risk of non-creation and a knowledge risk of bad timing (for knowledge creation cluster), knowledge spillover or knowledge forgetting (for knowledge loss), as well as knowledge hiding and knowledge hoarding (for knowledge use). Our study fills a gap in the literature by suggesting a scale for measuring knowledge risks and grouping them by factor, and also a scale for measuring vulnerabilities and grouping them by type of vulnerabilty by means of principal components analysis. Current state of knowledge on vulnerabilities and knowledge risks comprises mainly theoretical approaches. Also, most studies do not discuss the concepts of knowledge risks and vulnerabilities together. The main novelty of our study lies in its quantitative approach to analysing knowledge-related vulnerabilities and risks. For this purpose, a questionnaire was applied to a sample of employees comprising both managers and non-managers from Romanian organizations. The factors resulted after having applied the principal components analysis have provided a clearer picture of the constructs of vulnerabilities and knowledge risks, leading to a deeper understanding of knowledge processes and their risks.

The aim of this paper is to propose and test a new measuring scale for the concept of “knowledge vulnerabilities”. With this research, we are continuing our work on a research project concerning knowledge risks and knowledge vulnerabilities that can manifest in organizations, alongside knowledge processes. In the context of a scarce literature concerning knowledge vulnerabilities, this research adds value by understanding and measuring knowledge vulnerabilities, as weak points of the knowledge system and of the knowledge management routines that could trigger or facilitate the appearance of risks, exposing the organization to threats. In this paper, we present an instrument for measuring knowledge vulnerabilities related to the knowledge processes within an organization, in the form of a questionnaire. This instrument is developed starting from a previously tested instrument, with 21 items, proposed by Neștian and Guță (2023). The new instrument contains 42 items, with newly developed items reflecting a deeper understanding of knowledge vulnerabilities. The taxonomy for knowledge vulnerabilities is based on a recent perspective of organizational knowledge dynamics (Bratianu, Neștian and Guță, 2022). In this research, seven knowledge processes are considered and vulnerabilities related to them are proposed: vulnerabilities related to knowledge creation, vulnerabilities related to knowledge acquisition, vulnerabilities related to knowledge loss, vulnerabilities related to knowledge sharing, vulnerabilities related to knowledge use, vulnerabilities related to emotional knowledge dynamics and vulnerabilities related to spiritual knowledge dynamics. The questionnaire has been applied in private companies, on both managers and non-managers. Principal components analysis was undertaken to highlight the components forming the construct of vulnerabilities related to knowledge processes. The factors resulting from applying the statistical method of principal components analysis on the data collected with this improved questionnaire are composed starting from a larger number of items and are better aligned with the theoretical perspective used, compared with the questionnaire developed in 2023 by Neștian and Guță. The items of the questionnaire loaded in the principal components can be used for relevant future studies on knowledge vulnerabilities. The resulting components offer a better picture regarding the conceptualization of the notion of vulnerabilities associated with knowledge processes.