Generative AI Performance with DeepSeek: Key Service Quality Attributes from Users

In the rapidly evolving Generative AI (GenAI) consumer market, DeepSeek, a Chinese artificial intelligence (AI) startup, has emerged as a disruptive force, captured global attention in early 2025 by leading app download rankings and triggering a notable decline in US tech stocks. Amidst widespread media coverage and comparisons against established competitors like ChatGPT, the specific performance attributes of GenAI that matter to users remain underexplored. This study addresses the gap by identifying the key service quality attributes of GenAI that are valued by consumers using DeepSeek. Content analysis on 953 DeepSeek user comments was done using Leximancer to reveal 11 key themes: “AI”, “better”, “app”, “voice”, “feature”, “China”, “using”, “OpenAI”, “accurate”, “update”, and “perfect”. The findings reveal that users highly prioritize attributes such as conversational interaction, multilingual capabilities, and information accuracy, which collectively shape their perception of service quality. Notably, the theme “better” highlights users’ comparative evaluations of DeepSeek against rivals like ChatGPT and Gemini, emphasizing its open-source nature and cost-effectiveness. Meanwhile, themes like “voice” and “feature” underscore the demand for multimodal functionality and continuous innovation, while “China” reflects geopolitical and data privacy considerations influencing user trust. The study extends service-quality perspectives to GenAI by articulating construct-level implications (e.g., multimodality, data-governance trust, comparative value) and translating them into concrete design and governance actions for practitioners. It extends beyond traditional frameworks like SERVQUAL by identifying a set of dimensions unique to AI-driven environments. For practitioners, the findings provide actionable insights for developers and managers to enhance user experience, prioritize feature updates, and address privacy concerns. By aligning GenAI development with consumer expectations, this research supports the creation of more competitive and user-centric AI applications. Future research directions include cross-platform comparisons and longitudinal studies to track evolving service quality standards in the dynamic GenAI landscape.

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2025 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE)

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979-8-3315-4435-5

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