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Elfindah Princes
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Agriculture, Biological Sciences & Forestry Computer Science & IT Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science

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Journal : Journal of Mutidisciplinary Issues

 1 
The Impact of Online Platforms on Generation Z's Learning Styles and Educational Outcomes: A Comprehensive Study Elfindah Princes
Journal of Multidisciplinary Issues Vol 4 No 1 (2024): Journal of Multidisciplinary Issues (JMIS)
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v2i2.50

Abstract

Purposes - This study aims to investigate the relationship between material comprehension and satisfaction among Generation Z learners using online platforms. Specifically, it examines how variations in material comprehension influence overall satisfaction with the learning experience. The research also explores possible indirect effects of other variables, such as motivation, on this relationship. Methodology - The study employed a quantitative research design, collecting data from 51 respondents. A path coefficient analysis was conducted to evaluate the direct and indirect effects of material comprehension on satisfaction. The data were analyzed using bootstrapping methods to determine the significance of the relationships between the variables. Findings - The analysis revealed that material comprehension significantly impacts satisfaction (p-value = 0.002), with higher levels of comprehension leading to greater satisfaction. Additionally, learning motivation was found to have both direct and indirect effects on satisfaction through its influence on material comprehension. However, some anomalies were observed where high comprehension did not always correlate with high satisfaction, indicating the influence of other factors. Novelty - This research contributes to the growing body of literature on Generation Z learning by focusing on the relationship between comprehension and satisfaction in online learning environments. The study also highlights the importance of understanding the role of motivation in enhancing both comprehension and satisfaction, offering new insights into the dynamics of digital education. Research Implications - The findings have important implications for educators and policymakers, suggesting that efforts to improve material comprehension can significantly enhance student satisfaction in online learning environments. Moreover, the study highlights the need to address motivational factors to maximize learning outcomes. Future research should explore additional variables that may mediate or moderate the relationship between comprehension and satisfaction.    
Navigating the Challenges of AI-Generated Content: Examining Public Trust, Accuracy, and Ethical Implications Elfindah Princes
Journal of Multidisciplinary Issues Vol 4 No 1 (2024): Journal of Multidisciplinary Issues (JMIS)
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v2i2.54

Abstract

Purposes - The purpose of this research is to analyze the impact of the growth of AI-generated content on the accuracy and reliability of online information. Specifically, the research examines the challenges in detecting AI content, considering the limitations of AI tools like ZeroGPT and OpenAI’s Text Classifier, and explores how these challenges may influence public trust in online information. Methodology - This study employs a mixed-method approach combining quantitative data collection through surveys and qualitative case study analysis of AI-generated content controversies, such as articles from CNET and Microsoft. Data was analyzed using Structural Equation Modeling (SEM) to evaluate the relationships between AI usage and user trust. Findings - The results indicate that while there is a positive relationship between AI usage and public trust, the impact is not statistically significant. Issues like model collapse and AI inbreeding contribute to the challenge of maintaining content accuracy, which in turn affects the trustworthiness of AI-generated information. Novelty - This research contributes to the growing body of knowledge on AI-generated content by focusing on its impact on public trust, a relatively underexplored area. The study also introduces the concept of "model collapse" and "AI inbreeding" as critical factors that may undermine the reliability of AI-generated information. Research Implications - The findings have practical implications for media industries and AI developers. Enhancing AI algorithms to improve content accuracy and reliability, combined with stronger human oversight, could help mitigate the risks associated with AI-generated content and restore public trust in online information. The study also calls for the development of more advanced detection tools and ethical guidelines to govern the use of AI in information dissemination.
Impact of AI-Generated Content on AI Technology: Exploring Model Collapse and Its Implications Elfindah Princes
Journal of Multidisciplinary Issues Vol 4 No 1 (2024): Journal of Multidisciplinary Issues (JMIS)
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v2i2.55

Abstract

Purposes - This research aims to investigate the phenomenon of "model collapse" within Generative Adversarial Networks (GANs) when AI models are trained using AI-generated content. The study focuses on understanding the implications of model collapse on the quality of AI outputs, exploring new concepts like "Model Autography Disorder" (MAD) and "Habsburg AI," and discussing the broader ethical and social impacts of AI self-consumption. Methodology - The study utilizes a mixed-methods approach, combining simulation experiments with qualitative interviews. GAN models were trained on AI-generated data to simulate model collapse, and various techniques were applied to mitigate this collapse. Expert interviews provided insights into the ethical considerations and future directions for generative AI development. Findings - The research demonstrates that model collapse significantly impacts the performance and diversity of AI outputs when trained on synthetic data. Although some mitigation techniques show potential, they do not fully prevent the collapse. Concepts like MAD and Habsburg AI offer deeper understanding into the risks of AI self-consumption and its broader implications for AI-driven systems. Novelty - The introduction of new terms like "Model Autography Disorder" and "Habsburg AI" adds unique perspectives to the discourse on AI sustainability. The study is among the first to examine the ethical and technical challenges posed by AI self-consumption and its long-term effects on AI-generated content. Research Implications - This study underscores the necessity for stricter guidelines on using AI-generated content in training models to prevent model collapse. It also highlights the need for hybrid training methods and ongoing ethical considerations to ensure the quality, reliability, and sustainability of AI-driven systems.      
Systematic Literature Review: Gamification for Promoting Healthy Food Purchases Elfindah Princes
Journal of Multidisciplinary Issues Vol 3 No 1 (2023): Journal of Multidisciplinary Issues
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v3i1.56

Abstract

Purposes - This systematic literature review explores the application of Gamification in promoting healthy food purchases within the context of digital marketing. The purpose of this review is to investigate how Gamification strategies, such as points, badges, leaderboards, and rewards, have been utilized to influence consumer behavior, particularly encouraging healthier dietary choices. Methodology - The methodology follows PRISMA guidelines, involving a comprehensive search of relevant studies published between 2010 and 2023. A total of 35 peer-reviewed articles were analyzed to identify key themes, mechanisms, and outcomes associated with Gamification in digital marketing and health promotion. Findings - The findings suggest that Gamification effectively increases consumer engagement and promotes healthy food purchases by using a combination of behavioral, reward, and social mechanisms. Novelty - The novelty of this review lies in its focus on the intersection of Gamification and health promotion, particularly in the underexplored area of encouraging healthy food consumption. Conclusion - The conclusion emphasizes that Gamification has the potential to be a powerful tool for behavior change in the digital marketing of healthy foods. However, further research is needed to explore the long-term effects of these strategies. Research Implications - The research implications highlight the importance of integrating Gamification elements into health-focused marketing campaigns to improve consumer engagement and foster sustained behavior change. Digital marketers and health practitioners can leverage these findings to design more effective interventions promoting healthier dietary habits.
Understanding the Generation Z Behaviour Intention to Purchase on Social Media: A Unified Theory of Acceptance and Use of Technology (UTAUT) Approach Annisa Alvionita; Elfindah Princes
Journal of Multidisciplinary Issues Vol 3 No 2 (2023): Journal of Multidisciplinary Issues (JMIS) 
Publisher : APPS Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53748/jmis.v3i2.58

Abstract

Purposes - The purpose of this study is to examine the significant impact of four factors—Performance Expectancy, Effort Expectancy, Social Influences, and Facilitating Conditions—on Behavioral Intention to use an application. Specifically, the study seeks to identify which of these factors significantly influence users' behavioral intentions and compare the findings to prior research in the field. Methodology - The study employs a quantitative approach, using a survey to gather data from application users. Hypothesis testing is conducted using structural equation modeling (SEM) to analyze the relationships between the constructs. The decision-making process is based on p-values and t-statistics, with hypotheses being accepted or rejected based on predefined thresholds: p < 0.05 and t-statistic > 1.96 for acceptance of the alternative hypothesis (Ha). Findings - The study reveals that Performance Expectancy and Facilitating Condition have significant positive impacts on Behavioral Intention. Specifically, Performance Expectancy shows a significant positive relationship with Behavioral Intention, with a path coefficient of 0.448 and a t-statistic of 2.736. Similarly, Facilitating Condition also significantly impacts Behavioral Intention, with a path coefficient of 0.337 and a t-statistic of 2.137. However, Effort Expectancy and Social Influences are found to have no significant impact on Behavioral Intention, as evidenced by their lower t-statistics and non-significant path coefficients. Novelty - The novelty of this research lies in the context-specific examination of the influence of Facilitating Condition and Performance Expectancy on Behavioral Intention, as well as the investigation into the non-significance of Effort Expectancy and Social Influences. This study provides updated insights that diverge from previous research, such as that of Oliveira et al. (2016) and Al-Okaily et al. (2020), which reported significant effects for Effort Expectancy and Social Influences. Research Implications - The findings suggest that developers and marketers should focus on enhancing Performance Expectancy and improving Facilitating Conditions to increase users' behavioral intentions to adopt and use the application. Efforts to improve the usability and social appeal of the application may not be as critical as previously thought, according to the results of this study. These insights can guide future application development and marketing strategies, as well as contribute to the ongoing academic discussion on technology acceptance models.
Co-Authors Annisa Alvionita Merryta Djakaria Randy Omar