Conference Information

ICEME 2026: International Conference on E-business, Management and Economics

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ICEME
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Submission Date:
2026-05-30 Extended
Notification Date:
2026-06-10
Conference Date:
2026-07-10
Location:
Beijing, China
Years:
17
Viewed: 18810   Tracked: 6   Attend: 2

Conference Partner Index (CP-I)

52.0 / 100
Ranked #1,149 of 5,651 conferences · Top 21%

#6 of 71 in Management & Social Sciences #57 of 253 in Information Systems & Web

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
78
Community attention (10%)
37
Public record completeness (15%)
35

Inputs used: Editions on record: 17 · Researchers following it here: 6 · Researchers who opened this page in the past 24 months: 10

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-05

Call For Papers

ICEME 2026 (International Conference on E-business, Management and Economics) is an academic conference held in Beijing, China on 2026-07-10. The paper submission deadline is 2026-05-30 (extended). Acceptance notifications are sent on 2026-06-10.

The topics of interest include, but are not limited to: 1. AI in Consumer Behavior Analysis and Personalization Understanding Consumer Preferences with AI Personalized Marketing Strategies using Machine Learning AI for Dynamic Pricing Models in E-Commerce Behavioral Segmentation Using AI Tools 2. AI for Optimizing E-Commerce Supply Chain and Inventory Management Demand Forecasting using AI and Big Data AI for Inventory Optimization and Stock Replenishment Smart Logistics: AI and Automation in E-Commerce Shipping AI-Driven Supply Chain Resilience 3. AI in E-Commerce Fraud Detection and Cybersecurity Machine Learning for Fraud Detection in Digital Transactions AI for Cybersecurity: Protecting Data and Consumer Trust AI-Enhanced Identity Verification Systems Predictive Risk Analysis for E-Commerce 4. AI-Enhanced Smart Commerce Systems Generative AI for Cross-Border Supply Chain Optimization Intelligent Pricing Algorithms and Dynamic Market Forecasting Blockchain-AI Synergy in Transparent Trade Systems Smart Decision Models for Sustainable Energy Economics 5. Secure AI-Driven Transactions Privacy-Preserving User Analytics via Federated Learning Cryptographic Solutions for Cross-Border Payments and Smart Contracts AI-Powered Fraud Detection and Digital Identity Verification E-Commerce Security Architectures in the Quantum Computing Era 6. Cognitive Technologies in Business Intelligence Multimodal Cognitive Computing for Consumer Behavior Prediction Large Language Models (LLMs) in Intelligent Customer Service Systems Neuroeconomics and Brain-Computer Interface Integration Explainable AI (XAI) for Financial Auditing and Decision Support 7. Intelligent Automation in Business Process Management AI-Driven Workflow Automation for Supply Chain Resilience Robotic Process Automation (RPA) in Financial Auditing and Compliance Natural Language Processing (NLP) for Contract Analysis and Risk Mitigation Ethical AI Governance in Automated Decision-Making Systems 8. Computational Intelligence for Sustainable E-Commerce AI-Optimized Carbon Footprint Tracking in Global Trade Reinforcement Learning for Energy-Efficient Logistics Networks Predictive Analytics in Circular Economy Business Models Bias Mitigation in AI-Driven Consumer Demand Forecasting 9. Industry-Specific Empirical Studies: Implementation Cases of Intelligence-Driven Business and Economic Value Manufacturing industry: Practical pathways of "production data-efficiency improvement-cost optimization" in smart factories (including industrial internet and predictive maintenance) Financial industry: Data-driven balance between risk and return: Empirical research on intelligent risk control, algorithmic wealth management, and digital payment Retail industry: Omnichannel data collaboration: Case analysis of precision marketing, inventory optimization, and customer Lifetime Value (LTV) enhancement Healthcare and agriculture: Intelligent conversion of health data (personalized diagnosis and treatment) and value realization of agricultural data (yield prediction, pest and disease monitoring) Other related issues
Last updated by Dunn Carl on

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