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All Journal International Journal of Supply Chain Management BASKARA: Journal of Business and Entrepreneurship Jurnal Teknimedia: Teknologi Informasi dan Multimedia Advance Sustainable Science, Engineering and Technology (ASSET)
Elfindah Princes
Bina Nusantara University
Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Transportation Other

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Facing Disruptive Challenges in Supply Chain 4.0 Elfindah Princes
International Journal of Supply Chain Management Vol 9, No 4 (2020): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

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Abstract

Modern technology has set the business competition to a much higher level. With the increasing number of population all around the world, the proper supply chain management to ensure products availability is inevitably required. Modern Manufacturing industry must put in extra efforts in all fields, especially in the supply chain management system to make the products ready on time, at the right place, for designated customers. This will certainly be not easy because the world is changing continuously. Using qualitative research, this paper will discuss the disruptive challenges faced by modern manufacturing industry and the preparations need to be done by all parties included in Supply Chain 4.0. The findings show that Customer Experience will be the first brand differentiator in the future and must be given serious attention if we want to maintain firm performance.
Integrating Ambidexterity into the Modern Manufacturing Era of Industry 4.0 Elfindah Princes
International Journal of Supply Chain Management Vol 9, No 4 (2020): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

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Abstract

Ambidexterity has been a great solution for many problems so far especially in the world of industry. The swift and unpredictable changes in the market have pushed the industry to integrate ambidextrous capabilities in all aspects, in this case Modern Manufacturing process. Manufacturing talks about the Supply Chain Management from the beginning until the products reach the markets. There have been so many debates about how Ambidexterity will affect the Modern Manufacturing, if it creates burdens or instead it will give positive effects. The previous research has been contradictory between one another. Using systematic literature research and qualitative approach, this paper aims to analyze the needs of integrating ambidexterity into the Modern Manufacturing Era of Industry 4.0 to increase the competitive advantage for the company. The researcher concluded that despite all the debates, ambidexterity is a not an option for the future competitions, it is a must to solve disruptive problems due to technology advancement. There are important steps need to be taken in order to integrate ambidexterity capabilities into the company with clear deadlines and goals. We must also pay attention to the transition process in integrating ambidexterity in the manufacturing industry which may be different from other industries. The limitation of this research is there is no obvious and real example of successful manufacturing companies that have succeeded and taking detailed notes of the transition to further confirm the findings in this paper. Future research should address this.
THE ROLE OF PERSONALIZATION, RECOMMENDATION SYSTEMS, INFORMATION QUALITY, AND E-SERVICE QUALITY IN IMPROVING SHOPEE USER SATISFACTION: AN SEM-PLS APPROACH Graviela Charleen; Elfindah Princes
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.396

Abstract

User satisfaction has become a crucial factor in the success of e-commerce platforms amid increasingly fierce competition, particularly for Shopee as the platform with the highest number of visits in Indonesia. This study aims to analyze the influence of personalization, recommendation systems, information quality, and electronic service quality (e-service quality) on Shopee user satisfaction through the mediation of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) within the Technology Acceptance Model (TAM) framework. The research method employed is a quantitative approach with a survey of 430 active Shopee users in the Jabodetabek area who have completed at least two transactions in the last three months. Data were analyzed using Partial Least Square-based Structural Equation Modeling (SEM-PLS) with SmartPLS software. The results show that the recommendation system is the strongest predictor of PU, while e-service quality is the main determinant of PEOU. PU has the most dominant direct influence on user satisfaction, followed by PEOU. All mediation paths proved to be significant, with the recommendation system having the strongest indirect effect through PU. The research model can explain 73.1% of the variance in user satisfaction. It can be concluded that the integration of intelligent technology and basic service quality simultaneously shapes perceptions of usefulness and ease of use, which become the main pillars of e-commerce user satisfaction in Indonesia.
Implementation of the S-O-R Framework in Analyzing Consumer Behavior on TikTok Platform Toward Repurchase Intention Muhammad Adhie Putra Rivaldy; Afa Ahmad Yunus; Mochamad Rifky Rifaldi; Elfindah Princes
BASKARA : Journal of Business and Entrepreneurship Vol. 8 No. 1 (2025): BASKARA: Journal of Business and Entrepreneurship
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54268/baskara.v8i1.27913

Abstract

This research explores how consumers behave on the TikTok Shop platform using the framework of Stimulus-Organism-Response (SOR); this is done to identify the influence of digital stimuli on repurchase intention. This study has the objective to examine how brand authenticity, influencer credibility, and social media engagement as external stimuli influence brand trust and customer satisfaction as internal responses, and how these, in turn, shape consumers’ repurchase intention on TikTok Shop. The study applies a quantitative explanatory method, with respondent data collected from 550 TikTok Shop consumers in the Jabodetabek area, an urban region with high digital engagement. PLS-SEM via SmartPLS was applied to analyze the data. The findings show that brand authenticity significantly affects both brand trust and customer satisfaction. However, influencer credibility and social media engagement do not show significant effects. Both brand trust and customer satisfaction positively influence repurchase intention. These results provide a bigger understanding of psychological mechanisms in social commerce and give ideas for digital marketers seeking to enhance consumer retention through authentic branding and trust-based strategies
A Decision Support System Based on Transformer-Driven Sentiment Analysis of Social Media Data Arie Christian Wibisono; Elfindah Princes
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.3123

Abstract

The growing availability of social media data offers new opportunities for decision support systems (DSS) in large-scale human resource screening. This study proposes a technology-driven DSS architecture integrating transformer-based sentiment analysis to support early-stage candidate profiling. Its novelty lies in combining IndoBERT-based sentence embeddings with a structured DSS layer that aggregates tweet-level sentiment into risk-aware recommendations, rather than treating sentiment classification as a standalone output. Using a quantitative experimental design, 5,000 public posts from 100 users were processed through an NLP pipeline incorporating mean-pooled embeddings, feature engineering, principal component analysis, and Support Vector Machine classification. The model achieved 69.1% accuracy, with weighted precision, recall, and F1-score of 0.694, 0.691, and 0.691, outperforming baseline models by 6.5–15.0 percentage points. Sentiment outputs are treated as probabilistic behavioral signals within an advisory DSS framework, not direct indicators of candidate suitability. Preliminary validation on 50 cases showed moderate correlations (ρ = 0.52–0.61) with conventional assessments. The system remains non-automated, incorporating confidence thresholds, uncertainty handling, and mandatory human oversight. Limitations include moderate accuracy, reliance on text-only data, and linguistic ambiguity.
Co-Authors Afa Ahmad Yunus Arie Christian Wibisono Graviela Charleen Mochamad Rifky Rifaldi Muhammad Adhie Putra Rivaldy