cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
debora christine sianturi
Contact Email
jembtjembt891@gmail.com
Phone
+6281360000891
Journal Mail Official
jembtjembt891@gmail.com
Editorial Address
Romeby Lestari Housing Complex Block C Number C14, North Sumatra, Indonesia
Location
Unknown,
Unknown
INDONESIA
Journal on Economics, Management and Business Technology
Published by Ihsa Institute
ISSN : -     EISSN : 29620694     DOI : -
Core Subject : Economy, Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering
Journal on Economics, Management and Business Technology, is a Economics, Management and Business Technology published since 2022 by IHSA Institute. Journal on Economics, Management and Business Technology published 2 times a year (March and September), Each issue consists of a minimum of 5 articles, the scope of this journal is Economics, Management and Business Technology.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 42 Documents
 1   2   3   4   5 
Analysis of the Influence of AI Recommendation Systems on Consumer Purchasing Decisions: The Mediating Role of Personalization and Consumer Trust Gaishan Raffasya Hafis
Journal on Economics, Management and Business Technology Vol. 4 No. 2 (2026): March: Economics, Management and Business Technology
Publisher : IHSA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid advancement of Artificial Intelligence (AI) has transformed the e-commerce industry by enabling businesses to deliver personalized product recommendations that improve customer experiences and support data-driven marketing strategies. AI recommendation systems have become a fundamental component of digital retail because they analyze consumers' browsing histories, purchasing behaviors, and preferences to generate relevant product suggestions. This study aims to analyze the influence of AI recommendation systems on consumer purchasing decisions and examine the roles of personalization and consumer trust within the recommendation process. This research employed a quantitative explanatory research design using a cross-sectional survey of 312 e-commerce users who had previously purchased products based on AI-generated recommendations. Respondents were selected through purposive sampling, and data were collected using a structured questionnaire with a five-point Likert scale. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software to evaluate both the measurement and structural models. The findings indicate that AI recommendation systems have a significant positive effect on consumer purchasing decisions. The results further demonstrate that personalized and trustworthy recommendations reduce consumers' information search effort, increase purchase confidence, and improve overall shopping experiences. In conclusion, AI recommendation systems enhance purchasing decisions when recommendations are perceived as relevant, personalized, accurate, and trustworthy. These findings contribute to the literature on AI-driven consumer behavior and provide practical guidance for e-commerce platforms and digital marketers seeking to improve recommendation strategies, strengthen consumer trust, and enhance customer engagement in increasingly competitive digital marketplaces.
The Impact of Predictive Analytics on Business Decision Effectiveness: Evidence from Data-Driven Organizations Shezan Ashilah Vandana
Journal on Economics, Management and Business Technology Vol. 4 No. 2 (2026): March: Economics, Management and Business Technology
Publisher : IHSA Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid growth of big data, artificial intelligence (AI), and digital transformation has accelerated the adoption of predictive analytics across organizations, enabling businesses to extract valuable insights from large volumes of structured and unstructured data. Organizations increasingly utilize predictive analytics to forecast future market trends, optimize operational processes, improve customer relationship management, and support evidence-based decision-making in highly competitive business environments. Consequently, understanding the influence of predictive analytics on business decision effectiveness has become increasingly important for enhancing organizational competitiveness and long-term performance. This study aims to analyze the impact of predictive analytics on the effectiveness of business decisions and examine how predictive capabilities improve managerial decision-making. A quantitative explanatory research design was employed using data collected through structured questionnaires administered to 250 business managers, executives, and data analysts from various industries. The collected data were analyzed using Structural Equation Modeling based on Partial Least Squares (SEM-PLS) to examine the relationships between predictive analytics and business decision effectiveness. The findings reveal that predictive analytics has a positive and statistically significant effect on business decision effectiveness. Specifically, higher predictive capability improves decision quality, decision speed, decision accuracy, resource optimization, and organizational performance while reducing uncertainty and operational risks. The study concludes that predictive analytics represents a strategic organizational capability that enables more accurate, timely, and data-driven decision-making. Strengthening data quality, analytical capabilities, technological infrastructure, and the integration of predictive insights into business processes can significantly improve organizational competitiveness and support sustainable business performance in an increasingly data-driven economy.

Page 5 of 5 | Total Record : 42

 1   2   3   4   5 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2026): March: Economics, Management and Business Technology Vol. 4 No. 1 (2025): September: Economics, Management and Business Technology Vol. 3 No. 2 (2025): March: Economics, Management and Business Technology Vol. 3 No. 1 (2024): September: Economics, Management and Business Technology Vol. 2 No. 2 (2024): March: Economics, Management and Business Technology Vol. 2 No. 1 (2023): September: Economics, Management and Business Technology Vol. 1 No. 2 (2023): March: Economics, Management and Business Technology Vol. 1 No. 1 (2022): September: Economics, Management and Business Technology