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生成式AI在服务业营销场景的应用特征与挑战

Application Characteristics and Challenges of Generative AI in Service Industry Marketing Scenarios

Progress in Social Sciences / 2025,7(7): 566-572 / 2025-07-30 31 19

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• Authors: 曾育新 覃艳华 陈纯炼
• Information:

  电子科技大学中山学院管理学院，中山

• Keywords:
  Generative artificial intelligence (AIGC); Service marketing; Human-AI collaboration; Cultural adaptation; LEAF model; Data security; Empathy gap; Value co-creation
  生成式人工智能（AIGC）; 服务营销; 人机协同; 文化自适应; LEAF模型; 数据安全; 共情鸿沟; 价值共创
• Abstract: Generative Artificial Intelligence (AIGC) is profoundly reshaping the value creation paradigm in service marketing. Grounded in Service-Dominant Logic (S-D Logic), this study systematically analyzes AIGC’s application characteristics across three core service marketing scenarios: Intelligent Interaction: Constructing a human-AI-customer triadic collaboration system (Guo, 2025) to enable seamless service delivery. Content Creation: Achieving scalable personalized service (Bai & Guo, 2024) through dynamic content generation. Customer Insight: Empowering data-driven decision-making for precision marketing. These applications significantly enhance marketing efficiency, personalization, and experiential quality. Concurrently, the study identifies critical inherent tensions in localized AIGC implementation: Technology-Trust Tension: Data security risks and algorithmic black-box opacity undermining accountability (Zhang & Chen, 2025; Chen & Li, 2025). Efficiency-Empathy Tension: The “empathy gap” in human-AI interaction, particularly detrimental in high-touch service recovery scenarios (Li & Zheng, 2024). Globalization-Localization Tension: Cultural misfit due to inadequate adaptation of global models to local contexts (e.g., Chinese cultural nuances) (Wang & Wang, 2025). To address these challenges, we propose the innovative Lightweight-Embedded-Adaptive-Forward-feeding (LEAF) Model: Lightweight Technology (L): Prioritizing API integration (e.g., Baidu, Alibaba, Tencent, DeepSeek) for cost-effective access. Embedded Processes (E): Structuring workflows (e.g., “AI triage + human expert”, “AI draft + human refinement”) for genuine human-AI collaboration. Adaptive Culture (A): Implementing prompt engineering and localized fine-tuning to embed cultural intelligence. Forward-feeding Mechanism (F): Establishing a data-driven feedback loop (collection → structuring → model optimization) for continuous improvement. The LEAF model provides a strategic pathway for local service enterprises, especially resource-constrained SMEs, to implement AIGC responsibly, efficiently, and cost-effectively, offering significant theoretical and practical contributions. 生成式人工智能（AIGC）正深刻重构服务业营销的价值创造范式。本文基于服务主导逻辑（S-DLogic），系统分析AIGC在智能交互、内容创生与客户洞察三大核心场景的应用特征：通过构建“人—机—客”三元协同体系(郭蕾蕾，2025)、实现规模化个性化服务(白雪梅，郭日发，2024)、赋能数据驱动决策，显著提升营销效率与体验。同时，深入剖析本土化应用的内在张力：技术与信任矛盾（数据安全与算法黑箱风险）(张亮，陈希聪，2025；陈嘉鑫，李宝诚，2025)、效率与情感冲突（“共情鸿沟”）(李森，郑岚，2024)、全球化与本土化失衡（文化适配性缺失）(王闻萱，王丹，2025)。针对挑战，创新性提出“服务营销AI轻量化敏捷（LEAF）模型”，以技术轻量化（L）控制成本、流程嵌入化（E）协同人机、文化自适应（A）弥合文化差异、反馈前馈化（F）驱动动态优化。该模型为本土服务业企业（尤其中小企业）提供了低成本、高效率、负责任的AIGC战略实施路径，兼具理论创新与实践指导价值。
• DOI: https://doi.org/10.35534/pss.0707096
• Cite: 曾育新，覃艳华，陈纯炼．生成式AI在服务业营销场景的应用特征与挑战［J］．社会科学进展，2025，7（7）：566-572.