Journal of Information and Organizational Sciences (JIOS)
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455 research outputs found
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Resilience and Self-Efficacy: Keys to Students’ Change Readiness in Higher Education
No full textThis study aims to understand the influence of resilience, self-efficacy, and critical thinking on the readiness to change of final-year students, with the moderating roles of organizational culture, technological adaptation, and the mediating role of psychological empowerment in higher education. Data was gathered through an online questionnaire sent to respondents, consisting of 255 final-year students and 205 higher education members. The data was analyzed using Multi-level CFA. At the individual level, study investigates final-year students' readiness to change through resilience, self-efficacy, and critical thinking. At an organizational level, this study focuses on organizational culture, technological adaptation, and psychological empowerment. Organizational culture significantly enhances students' psychological empowerment, boosting readiness for change, as psychological empowerment is a mediator between organizational culture and readiness for change. Technological adaptation strengthens psychological empowerment, where students with higher tech proficiency show greater psychological empowerment and readiness for change. This finding underscores the value of integrating technology into education to improve learning engagement and adaptability
Validation of the TPACK Instrument in the Croatian Primary School Context
No full textThis study aimed to validate a generic TPACK questionnaire within the Croatian educational context, involving 609 in-service subject teachers from five regions. The survey-based research included exploratory and confirmatory factor analyses. The resulting structure diverged from the original seven-factor model, yielding five factors: pedagogical knowledge (PK), technological knowledge (TK), content knowledge (CK), technological content knowledge (TCK), and technological pedagogical content knowledge (TPACK). Some items were excluded to enhance validity. Pedagogical and pedagogical content knowledge merged, as did technological pedagogical and technological pedagogical content knowledge. The final five-factor model exhibited strong psychometric properties, characterized by high internal consistency and a good fit to the data. Evidence of convergent and discriminant validity supports the distinct yet interconnected nature of the identified knowledge domains. Significant positive correlations were found between all constructs, with the strongest correlations between TPACK and TCK, and also between TPACK and TK. The findings underscore the contextuality of the TPACK framework, and therefore, they were discussed in relation to recent national educational initiatives as well as international indicators on digital competencies. These results contribute to understanding the TPACK framework in the Croatian context, supporting future research on teacher competencies in integrating technology into education
Evaluating the Impact of 5G and 4G Networks on the Performance of Real-Time Health Monitoring Systems
No full textThis paper investigates the performance of 5G networks compared to 4G LTE, WiFi, and BLE for transmitting real-time health monitoring data. Using Apple Watch Series 7 and Fitbit Sense devices connected to commercial 5G and 4G networks, our experimental analysis demonstrates that 5G technology offers significant advantages for healthcare monitoring applications. Results show a 62% reduction in latency (8.2ms versus 21.6ms), 83.4% improvement in throughput, and 75% reduction in packet loss compared to 4G LTE networks. The low latency achieved with 5G (8.2ms) is particularly critical for remote cardiac monitoring, where transmission delays directly impact clinical response time. Signal strength correlation analysis reveals that 5G networks maintain performance consistency across varying RSRP levels, with only 16% performance degradation at -110dBm compared to 42% for 4G networks. Our findings confirm that 5G networks provide the reliability and performance required for next-generation real-time health monitoring systems, especially for applications requiring continuous vital sign monitoring and immediate clinical feedback
Does Empowering Leadership and Technology Matter for Readiness for Change? The Mediating Role of Organizational Commitment in Public Organization
No full textOrganizational change is an ongoing and dynamic process that enables institutions to adapt to both internal and external developments. This study explores key factors that influence employees' readiness to embrace change, focusing specifically on the roles of Empowering Leadership and Technology Readiness. The research examines how these factors directly and indirectly affect Readiness for Change, with Organizational Commitment acting as a mediating variable, as guided by a carefully developed conceptual and empirical model. A quantitative approach was used, employing a structured questionnaire survey distributed to change champions and change ambassadors at BPS Headquarters, Provincial Offices, and Regional Offices across Indonesia. Data from 432 valid responses were analyzed using Structural Equation Modeling (SEM) to test the proposed hypotheses and evaluate the overall model. Findings reveal that both Empowering Leadership and Technology Readiness have significant positive effects, both direct and indirect, on Readiness for Change. Organizational Commitment plays a critical mediating role in these relationships. This study contributes to theoretical literature by empirically demonstrating how leadership style and technological adaptability jointly enhances readiness in a public sector institution within a developing country. It highlights the human-centered dimensions necessary for sustaining transformation in bureaucratic settings
The Influence of Gamification on Repurchase Intention at E-Marketplace from a Habit Perspective
No full textThis study investigates how gamification influences habit formation and repurchase intention in Indonesian e-marketplaces, focusing on key elements such as points, rewards, badges, and challenges. Using the Stimulus-Organism-Response (SOR) model and habit formation theory, the research examines how user engagement drives repeat purchasing behavior. Data were collected from 375 Shopee and Tokopedia users via an online survey, and the hypotheses were tested using Structural Equation Modeling (SEM). The results reveal that gamification significantly enhances customer engagement, which in turn strengthens habitual use and positively impacts repurchase intention. Among the gamification elements, rewards emerged as the most influential driver of repeat purchases, suggesting that incentive-based mechanisms are particularly effective in promoting customer retention. Unlike previous studies that primarily emphasize engagement and loyalty, this research highlights the specific role of gamification in shaping behavioral habits that lead to sustained purchasing. By integrating habit theory into the SOR framework, the study offers a fresh perspective on long-term consumer behavior in digital commerce. These findings have practical implications for e-marketplace platforms seeking to optimize their gamification strategies to maintain engagement and boost sales. Overall, the study emphasizes the importance of habit formation as a key factor in enhancing customer retention through gamification
Factors Affecting Students’ Acceptance of Learning Simulation Tools in Computing Education Courses from Social, Technology, and Personal Trait Perspectives
No full textThis study presents a theoretical model to explore the factors influencing students' acceptance of simulation tools in computing education. These factors include social influences, technology-related aspects, and personal characteristics. The term "simulation tools" refers to systems that can replicate complex processes and situations, providing students with realistic, hands-on experiences without the risks or costs associated with physical setups. To validate the proposed model, 312 responses from university students were collected. A cross-sectional online survey was conducted, and the participants were selected through purposive sampling. The findings indicated that subjective norms have the most significant direct effect on students' perceptions of usefulness, influencing their views on learning outcomes from using simulation tools in computing education courses. Additionally, social support and self-efficacy were also found to have significant effects. However, the impacts of fidelity and innovativeness were not supported. This study sets itself apart from previous research by using a comprehensive approach to explore the factors influencing student acceptance of simulation tools in computing education. Specifically, this research develops a theory based on the Technology Acceptance Model (TAM) and expands it by incorporating environmental factors and personal characteristics of students
Bootstrap Forest based method for Encrypted Network Traffic Analysis
No full textEncrypting communications and data over the Internet becomes essential in ensuring the privacy of communications and protecting the data from increasing threats. Hence, majority of Internet traffic and networked communications are encrypted now. However, encryption also provides a means for attackers to hide them behind encrypted communications and conduct malicious activities. Analyzing the unencrypted communications is relatively easy. The same task is highly challenging due to the presence of encryption in network communication. Conventional network analysis methods fail to analyze encrypted communications. There are methods like flow monitoring that are available to detect encrypted traffic and analyze traffic flow related features. By using traditional analysis methods, we could not achieve accurate detection and classification of encrypted network packets in various types of network traffic such as VoIP, Text, Audio, Video, VPN traffic. In our work, we have proposed the Bootstrap Forest model to analyze and classify encrypted network traffic. Bootstrap Forest model accurately classifies the encrypted network traffic using statistical and time-based features. The performance of the proposed model is evaluated and compared with the performance of other machine learning models under various performance metrics. The three publicly available datasets such as UNSW-NB15, ISCXTor 2016 and ISCXVPN 2016 datasets were used in our experimentations and evaluations. The experimental results show that our proposed model provides the best performance for classifying encrypted network traffic while comparing the F1 score with other methods
The Evolution of Cloud through SJF-ML Hybrid Scheduling
No full textPurpose: The author proposes sixteen Shortest Job First - Machine Learning (SJF-ML) hybrid algorithms, combining the cloud's SJF scheduling algorithm with four ML algorithm categories, with cloud evolution through ML intelligence as the primary objective. The four categories include: SJF-CA, SJF-ELA, SJF-PM, and SJF-RA. The developed SJF-ML algorithms by the author perform pattern recognition of the tasks that are to be computed, to improve decision-making during task computations in the cloud. These sixteen SJF-ML algorithms include: SJF-ADAB, SJF-BAY, SJF-DT, SJF-KNN, SJF-LAS, SJF-LDA, SJF-LGB, SJF-LN, SJF-MLP, SJF-NAV, SJF-PLY, SJF-RDG, SJF-RF, SJF-RBST, SJF-SVM, and SJF-XGB. Performance Metrics: Cost, Time, Energy, and LB are utilized to compare the developed algorithms with baseline SJF, along with comparing them within their respective SJF-ML categories. Dataset: The real-time Google Big Data Task (BDT) dataset, comprising tasks ranging from one hundred to one thousand across nineteen files, was computed using the SJF-ML and SJF algorithms. Experiment: Open-source CloudSim simulator with VM counts of 20, 40, 60, 80, and 100 were utilized to compute the BDTs, outputting results across the considered metrics. Results: The algorithms SJF-XGB and SJF-LN provided the best results, with SJF-DT, SJF-LAS, and SJF-LDA providing poor results. Findings: Hybridization of the cloud's scheduling algorithms with ML provides improved intelligence and performance, resulting in the evolution of the cloud
Longitudinal Impacts of Job Insecurity on Life Satisfaction: Mediating Roles of Trust in Government and Hope in the European Union
No full textThis longitudinal study examines the relationships among job insecurity, life satisfaction, trust in government, and hope during the COVID-19 pandemic across 27 European Union countries. Using data from 8,750 participants collected via the PsyCorona Study, the analysis applies the PROCESS macro (model 6) with 5,000 bootstrapped samples to estimate indirect effects with 95% bias-corrected confidence intervals. Findings reveal that job insecurity significantly reduces life satisfaction, explaining 13.62% of the variance over time. Trust in government mediates this relationship in earlier waves, though its influence diminishes later. Conversely, hope consistently emerges as a strong mediator across all waves, accounting for 24.64% of the variance. Sequential mediation via trust and hope is significant early on but weakens by wave 22. These findings underscore the essential role of government trust and hope in buffering the negative effects of job insecurity and enhancing societal resilience during times of crisis