Human–computer negotiation has the potential to play an important role in today’s highly dynamic online environment, especially in business-to-consumer e-commerce transactions. However, the lack of research on effective automated negotiation algorithms to respond to human buyers’ strategic and/or tactic offers has limited the development of automated human–computer negotiation systems for real-world applications. Intelligent software agents that are capable of dynamically adjusting their negotiation strategy in response to human buyers’ offers can greatly improve the negotiation experience of human buyers. In this study, guided by design science principles, we design a portfolio strategy model, which implements four negotiation strategies (i.e., time-dependent, behavior-dependent, dynamic time-dependent, and impasse resolution) as the core of our software agent for negotiating with human buyers. To evaluate this novel model, we implement a prototype of the system and compare it with three benchmark single-strategy models (i.e., competitive, collaborative, and selection) in human–computer negotiation experiments. The results show that our model not only enables the software agent to outperform its human counterpart but also significantly increases the settlement ratio and the joint outcome of both parties.