Decision-Making Process in E-Commerce and Social Networks

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 207

Editors

School of Business, State University of New York at New Paltz, New Paltz, NY 12561, USA
Interests: decision-making; e-commerce; operations management; artificial intelligence; information systems; healthcare analytics

E-Mail Website
Guest Editor
School of Economics and Management, Anhui Polytechnic University, Wuhu 241000, China
Interests: AI service; information management

Special Issue Information

Dear Colleagues,

The rapid evolution of digital platforms has transformed how individuals and organizations make decisions in online environments. E-commerce systems and social networks now serve as dynamic ecosystems where consumer behavior, algorithmic influence, and real-time feedback loops converge to shape decision-making processes. Understanding these mechanisms is crucial for enhancing user experience, refining business strategies, and ensuring ethical and transparent digital interactions.

Over the past two decades, research in this domain has explored behavioral economics, recommender systems, trust modeling, and sentiment analysis. However, emerging challenges — such as algorithmic bias, misinformation propagation, and privacy-aware personalization — demand fresh perspectives and interdisciplinary approaches. The intersection of data science, psychology, marketing, and computational modeling offers fertile ground for innovation.

This Special Issue aims to bring together cutting-edge research that advances our understanding of decision-making in e-commerce and social networks. We welcome contributions that explore theoretical frameworks, empirical studies, and applied methodologies. Topics may encompass individual and group decision dynamics, platform design, AI-driven personalization, and the impact of social influence on digital choices.

Topics of Interest (include but are not limited to):

  • Decision-making models in online shopping and social platforms;
  • Influence of recommender systems and personalization algorithms;
  • Trust, reputation, and credibility in digital environments;
  • Behavioral economics and cognitive biases in e-commerce;
  • Sentiment analysis and opinion mining for decision support;
  • Social contagion and network effects on consumer behavior;
  • Privacy-aware decision modeling and ethical AI;
  • Real-time decision analytics and adaptive systems;
  • Multi-agent decision-making in social commerce;
  • Case studies in platform design and user engagement.

Dr. Ai Ren
Prof. Dr. Xiaodong Li
Guest Editors

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Keywords

  • decision-making
  • e-commerce
  • social networks
  • recommender systems
  • trust modeling
  • sentiment analysis
  • behavioral economics
  • personalization
  • social influence
  • digital platforms
  • network effects
  • ethical AI

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Published Papers (1 paper)

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Research

27 pages, 4800 KB  
Article
Collaborative Governance of Involutionary Competition in Platform Economy Under Traffic Contestation: A Case Study of China’s Food Delivery Platforms
by Yanhong Ma and Yumeng Zhong
Information 2026, 17(7), 651; https://doi.org/10.3390/info17070651 (registering DOI) - 4 Jul 2026
Abstract
The entry of JD.com into the food delivery sector and the ensuing subsidy competition have resulted in irrational competition, merchant profit squeezes, and food safety risks in China. This study therefore investigates the collaborative governance mechanisms for food delivery platforms under involutionary competition [...] Read more.
The entry of JD.com into the food delivery sector and the ensuing subsidy competition have resulted in irrational competition, merchant profit squeezes, and food safety risks in China. This study therefore investigates the collaborative governance mechanisms for food delivery platforms under involutionary competition driven by traffic contestation. A two-agent evolutionary game model between platforms and merchants is developed, and Q-learning simulations are conducted to capture dynamic learning behaviors. The analysis examines the effects of coupon face value, cost-sharing mechanisms, traffic incentives, and government incentive-penalty policies on the strategic choices of both agents. Key findings reveal that merchants are more sensitive than platforms to traffic incentives and government penalties. Traffic-dependent merchants and traffic-independent merchants exhibit significantly different responses to government interventions. The coupon face value demonstrates a threshold effect, where only a reasonable range encourages compliant behavior among both parties. Based on these results, a collaborative governance framework is proposed. For traffic-dependent merchants, the government should focus on regulating platform behaviors and supervising coupon value controls, while platforms should establish a reward-oriented, penalty-supported incentive mechanism. For traffic-independent merchants, the government should strengthen consumer-reporting penalty mechanisms and strictly control collusion risks between platforms and merchants. Platforms should increase inspection frequency and reinforce penalties to prevent, at the source, the decline in product quality and market disorder induced by involutionary competition. This study provides strategic insights for achieving collaborative governance of involutionary competition in platform economies under intense traffic contestation. Full article
(This article belongs to the Special Issue Decision-Making Process in E-Commerce and Social Networks)
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