Journal of Information and Organizational Sciences (JIOS)
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Fueling the Innovation Spark: How Employee Oriented HR Practices and Career Satisfaction Fosters Innovative Work Behavior?
No full textEmployee-oriented HR practices have ascertained their instrumentality in nurturing innovative work behavior (IWB) of employees via career satisfaction (CS). This study aims to investigate how employee-oriented human resource (HR) practices (salary, job enrichment, job stability, training) can influence career satisfaction that subsequently affects employees' innovative behavior. Anchoring on social exchange theory and signaling theory, the data for the study was collected from 358 employees of Small and Medium Enterprises (SMEs) by purposive sampling. The study applied Structural Equation Modeling (SEM) using SmartPLS 4 to test the proposed hypotheses. The findings of this study offer useful insights into the degree to which career satisfaction mediates the impact of employee-oriented HR practices on innovative work behavior. The results suggest a significant but moderate to weak; positive association between employee-oriented HR practices and their innovative activity. Furthermore, the research establishes the mediating impact of career satisfaction while investigating the mechanism through which employee-oriented HR practices foster innovative work behavior. The study expands the knowledge base of extant literature by illuminating the critical role of employee-oriented HR practices in driving employees’ innovative behavior at the workplace. Besides, it illuminates the mediating role of career satisfaction, accenting the necessity for companies to not only implement employee-oriented HR practices but also foster a sense of satisfaction within employees to unearth the full potential of innovative behaviors within the workforce
Digitalization of Education and Information Technologies as a Factor of Digital Economic Development
No full textThe purpose of this study is to analyze the use of information technologies for the development of the digital economy. Digitalization of education is a necessary condition for sustainable development of modern society. The article is devoted to analyzing the factors of digitalization of education, among which information technologies are the main ones. For the analysis, the authors used a model for the development of the digital economy, which was also developed in the training of a specialist in the agro-industrial complex. Education is viewed as a subsystem of the digital economy. It is shown that the digitalization of education has both positive and negative consequences not so much for the development of industry as for human development. The article draws conclusions about the features of the socio-anthropological crisis in the context of the digitalization of education. As conclusions, the article presents forecasts for the development of digitalization of education, which include the improvement of the development and methods of introducing information and communication technologies in education, a constant change in the forms of knowledge assessment, the successful use of information technologies in the training of a specialist.
Physical Internet Enabled Traceability Systems for Sustainable Supply Chain Management
No full textCurrent supply chain management (SCM) requires the control of physical and information flows in order to satisfy the customer, i.e. deliver the right product to the customer at the right place, at the right time, at the right price and at the lowest cost. SCM is inseparable from traceability which makes reliable the said flows, accelerates the transmission of information on these flows, allows to access a detailed knowledge of the movements, and makes the flows visible. In order to streamline and monitor, if possible, in real time and permanently these logistical processes, we propose the design and implementation of an Ontology-based traceability system based on an architectural model for the physical Internet using computing resources such as Cloud computing, Fog computing and Internet of Things (IoT) to achieve efficiency and sustainability goals. To evaluate our system, we were able to carry out all the queries that the user can express whether he is a customer, a supplier or a manager
Deep Learning Model for Predicting Spreading Rates of Pandemics, “COVID-19 as Case Study”
No full textThe outbreak of Coronavirus (COVID-19), especially SARS-CoV-2, has led to a catastrophic scenario in the course of the world. The cumulative prevalence of COVID-19 was increasing rapidly day by day. Machine learning (ML) and deep learning (DL) can be deployed to facilitate tracking disease, anticipating the increase in epidemic, and hence planning for coverage techniques to control its spread. This work is based on the application of an advanced mathematical model to examine and predict the increase in a pandemic. On the bases of time-series data, an advanced DL model has been implemented to predict the risk of COVID-19 spreading in Iraq. A hybrid approach is presented where two deep learning algorithms; LSTM and GRU are brought up together to achieve good prediction with rewarding levels of (MAE = 0.109), (MAPE = 0.191) and (RMSE = 0.134)
Organizational Culture as Determinant of Individual Perception of UTAUT Model: Case Study Credit Union in Indonesia
No full textThis study aims to employ the Competitive Value Framework (CVF) and its value drivers as indicators for individual perceptions derived from the Unified Theory of Acceptance and Use Technology (UTAUT). The data was analyzed using the SEM-PLS method. The Reflective Formative Second Order Two-Stage Approach was used for the analysis, where variables related to organizational culture (OC) were examined using formative methods, while variables such as Performance Expectancy (PE), Social Influence (SI), Effort Expectancy (EE), and Behavioral Intention To Use (BI) were analyzed reflectively. The results revealed a significant correlation between OC and BI via EE and SI. However, the relationship between OC and BI via PE was found to be insignificant. This study contributes to theories about organizational culture, and information technology acceptance. On a practical level, these findings can assist decision-makers in paying greater attention to organizational culture when implementing new technologies
Relevancy between Anchor Text and Wikipedia: A Web Search Framework
No full textThe overall volume of data available on the Internet is growing rapidly while finding relevant documents is becoming increasingly difficult. Moreover, queries entered by users are unique, unstructured and often ambiguous while the process has changed dramatically from standard query languages that governed by strict syntax rules to unstructured strings. In Web information retrieval, search paradigms used term occurrences to weight document content prior to any boosting stage. PageRank algorithm, for instance, was used integrated techniques to enhance post retrieval document relevancy to adequately compromise the overall process in two stages. Nevertheless, hypertexts in Web have been used for improving the quality of search results for the most common type of queries. Our main premise is that hypertexts play an important role for ranking documents in IR such as margining between user queries and consensus hypertext. We propose a new algorithm that uses term impact technology for compromising hypertext weighting in Web along with Wikipedia for efficiently find most relevant documents among large set of results. Our experimental results showed that Wikipedia could efficiently improve document relevancy rank when combined with hypertexts for exhibit robust and very good short-term process capability
Modeling Information Diffusion in Online Social Networks Using a Modified Firefly Algorithm
No full textThe dynamics and patterns of information propagation on online social networks are complex and challenging to model and to predict. This study proposes a novel algorithm for simulating the spread of information on online social networks using a swarm-based approach. The algorithm is based on the firefly algorithm, which incorporates a new term called Vantablack to represent the non-spreader nodes in the network. The proposed algorithm is validated on three real-world datasets extracted from Kaggle.com, covering different topics and domains. The proposed algorithm outperforms other baseline methods in terms of accuracy and efficiency in predicting information diffusion
From the Editor
No full textDear Readers,
It is my great privilege to welcome you to this new issue of the Journal of Information and Organizational Sciences (JIOS) as its newly appointed Editor-in-Chief. As a full professor at the Faculty of Organization and Informatics, I am honored to take the helm of this esteemed journal and guide it through its next phase of growth and development. To support this vision, we have refreshed the Editorial Board, bringing together accomplished scholars committed to enhancing the journal’s quality and impact.
This issue also marks the debut of JIOS’s new visual identity, symbolizing our renewed dedication to innovation, clarity, and accessibility. Beyond aesthetics, we are introducing significant operational improvements, focusing on reducing manuscript review times while maintaining our rigorous academic standards. These efforts aim to attract and publish high-quality research across the diverse domains JIOS covers.
Our commitment to open access remains a cornerstone of our mission. JIOS continues to be an inclusive platform that ensures all published work is freely accessible to readers worldwide, with no fees for authors. We firmly believe in the power of knowledge sharing without financial barriers.
This issue features a diverse and impactful collection of articles addressing cutting-edge topics. Those involve advanced predictive models for public health and financial markets, innovative machine learning and natural language processing approaches for sentiment analysis and cybersecurity, and algorithmic methods for modeling online information diffusion. They also explore organizational factors such as culture, trust, and HR practices that influence technology adoption, sustainability, innovation, and task performance in workplace environments. Additionally, this issue highlights trends in educational management and blended learning, offering scientometric insights into this evolving field. Together, these studies contribute to advancements in information systems, organizational sciences, and applied machine learning.
We hope you find these contributions both insightful and inspiring. Thank you for your continued interest in JIOS, and we look forward to your engagement as readers, authors, and reviewers. Together, we can advance knowledge and make meaningful contributions to our fields.
Sincerely,
Prof. Igor Balaban, Ph.D.
Editor-in-Chief
Journal of Information and Organizational Science
A Study on Comparative Analysis of Feature Selection Algorithms for Students Grades Prediction
No full textEducation data mining (EDM) applies data mining techniques to extract insights from educational data, enabling educators to evaluate their teaching methods and improve student outcomes. Feature selection algorithms play a crucial role in improving classifier accuracy by reducing redundant features. However, a detailed and diverse comparative analysis of feature selection algorithms on multiclass educational datasets is missing. This paper presents a study that compares ten different feature selection algorithms for predicting student grades. The goal is to identify the most effective feature selection technique for multi-class student grades prediction. Five classifiers, including Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting (GB), and k-Nearest Neighbors (KNN), are trained and tested on ten different feature selection algorithms. The results show that SelectFwe(SFWE-F) performed best, achieving an accuracy of 74.3% with Random Forests (RT) across all ten feature selection algorithms. This algorithm selects features based on their relationship with the target variable while controlling the family-wise error rate