Proposing a Design Theory for a Human-Learning-Guided Virtual Negotiator for Online Trading Platforms

Negotiation-based transactional mechanisms provide flexibility and economic benefits to both sellers and buyers on online trading platforms. Although automated negotiation is a highly desired feature among online platform providers, the complexity and uncertainty of human behavior in human-to-computer (HtC) negotiation make it a problematic solution. This study proposes a design theory for a human-learning guided virtual negotiator (HLG-VN) framework that emulates human learning using multiple machine learning (ML) techniques that collectively mimic four human learning activities: didactic, feedback, observational, and analogical learning. Following the design science research methodology, we built an instantiation system for the proposed design theory and empirically tested it using experiments based on HtC negotiations. The empirical results show that our system outperformed the benchmark system in terms of both economic and some key social-psychological outcomes. Furthermore, the experiment results confirm the effectiveness and correctness of the HLG-VN framework. The proposed design theory provides a theoretical base for using ML techniques to build a virtual negotiator agent for an automated negotiation system. Thus, various agents could be designed and developed based on the theory for online trading platforms, thus improving negotiation efficiency and reducing transaction costs.