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All Journal Green Inflation: International Journal of Management And Strategic Business Leadership Management Dynamics: International Journal of Management and Digital Sciences
Aytan Azizli
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Decision Sciences, Operations Research & Management

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Social Media Sentiment Analysis as a Predictor of Product Launch Success in the Digital Marketplace Adi Lukman Hakim; Aytan Azizli
Management Dynamics: International Journal of Management and Digital Sciences Vol. 1 No. 1 (2024): January: International Journal of Management and Digital Sciences
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/managementdynamics.v1i1.419

Abstract

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.
Development of a Handover Model in Medical Services to Improve Service Quality at Hospital X Cikarang Listia Evalina; Rokiah Kusumapradja; Tjipto Rini; Aytan Azizli
Green Inflation: International Journal of Management and Strategic Business Leadership Vol. 2 No. 4 (2025): November : Green Inflation: International Journal of Management and Strategic B
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/greeninflation.v2i4.630

Abstract

This study investigates the clinical handover process as a critical component of patient safety and healthcare service quality in hospital settings. Inconsistent communication, incomplete documentation, and the absence of standardized procedures were identified as major problems contributing to communication failures and potential adverse events. The objective of this research was to develop and test a structured and effective handover model that could improve communication accuracy and documentation reliability among healthcare professionals. A mixed methods design was employed, consisting of a qualitative phase (in-depth interviews, observations, and document analysis) to explore real-world handover practices, followed by a quantitative phase using a pretest–posttest design to evaluate the effectiveness of the proposed model. The study proposes an integrated handover model based on SBAR-IM (Situation, Background, Assessment, Recommendation, Intervention, Monitoring) strengthened by Quality Control (QC) and Mutual Confirmation (MC) mechanisms. The findings demonstrate a significant improvement in handover quality after implementation, with communication scores increasing from 74 to 84.5 and documentation scores improving from 48.3 to 63.0 (p < 0.05). Qualitative results further revealed reduced communication errors, improved clarity of clinical information, and enhanced interprofessional collaboration. This study concludes that the proposed SBAR-IM–based QC–MC model is effective, practical, and suitable for improving patient safety and healthcare service quality in hospital environments.
Co-Authors Adi Lukman Hakim Kusumapradja, Rokiah Listia Evalina Tjipto Rini