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论文编号：	16130
作者编号：	2120243823
上传时间：	2026/6/8 19:47:56
中文题目：	服务型中小企业AI技术落地路径——基于X企业的单案 例研究
英文题目：	Research on the AI Implementation Path of Service Oriented SMEs :A Single-Case Study of Firm X
指导老师：	任兵
中文关键字：	服务型中小企业；人工智能；技术落地；动态能力；解释性路径模型；案例研究
英文关键字：	Service-oriented small and medium-sized enterprises (SMEs); Artificial intelligence (AI); Technology Implementation; Dynamic capability; Explanatory Path Pattern; Case study
中文摘要：	新一代人工智能技术持续突破，正推动人类社会从数字经济时代大步迈向智能经济新时代。人工智能技术持续演进并加速渗透企业经营场景，服务型中小企业如何推动人工智能技术在客户互动、服务交付和运营管理等真实业务场景中落地，已成为数字化转型研究中的重要议题。与大型企业相比，中小企业在资金、人才、技术储备和组织体系上较为有限，但决策链条短、组织调整灵活、场景试探成本相对可控，其人工智能技术落地过程因而呈现出鲜明的情境性与过程性特征，同时，服务型企业的价值创造高度依赖客户互动、服务流程与交付体验，使其 AI 技术落地不仅涉及工具使用，还涉及服务场景、人员分工和客户运营方式的调整。现有研究多聚焦于中小企业人工智能采纳的影响因素、实施障碍及应用结果，对于企业如何在真实经营情境中，将人工智能由外部技术变化逐步转化为内部业务运行能力，并进一步形成外部应用能力、重构增长逻辑，仍缺乏系统的过程性解释。 本研究以服务型中小企业X企业为案例，在动态能力视角下，对中小企业人工智能技术落地的形成过程、阶段跃迁逻辑与整体路径机制进行分析，并将企业家在关键节点上的判断与推动作用作为辅助解释线索纳入研究。本研究采用回溯性单案例研究方法，并结合纵向过程分析思路，综合正式访谈、非正式访谈、企业公开资料、第三方平台信息及内部分析资料等多源数据，通过关键事件梳理、阶段划分、开放编码、持续比较与跨阶段整合分析，重构了X企业人工智能技术落地的演进过程，并在此基础上形成 Gioia 数据结构与解释性路径框架。 研究发现，X企业人工智能技术落地经历了前期基础与路径准备、AI引入与研发探索、AI应用落地与运营体系重构、AI能力外部扩展四个阶段。总体而言，服务型中小企业人工智能技术落地并非围绕单一技术工具展开的一次性采纳行为，而是一条由旧路径受阻触发、经内部运行能力形成并在市场检验中持续校准的动态演化路径。具体来看：第一，服务型中小企业人工智能技术落地不是线性推进的技术应用过程，而是在问题重估、机会建构、战略承诺、路径收敛、关键场景突破与外部化应用共同作用下逐步推进的；第二，企业在旧路径承压、既有调整空间收窄的背景下，逐步将人工智能由外部技术现象建构为新的发展机会，并在资源约束与既有组织基础共同作用下形成持续推进的战略行动；第三，人工智能技术落地是一个由技术应用走向内部运行能力形成、再由内部运行能力走向外部应用能力与增长逻辑重构的持续转化过程。在这一过程中，企业所形成的关键能力主要体现为情境感知与机会识别能力、战略承诺与资源整合能力以及场景嵌入与体系重构能力，三者在不同阶段中持续推动了整体路径的形成与调整。 本研究构建了服务型中小企业人工智能技术落地的解释性路径框架，将其概括为“前置基础激活、机会意义建构、战略承诺与路径收敛、关键场景嵌入与内部运行能力形成、外部应用与增长逻辑重构”的动态演化过程。研究表明，中小企业人工智能技术落地是在现实压力、资源约束、关键场景牵引与市场持续检验共同作用下，由企业家推动与组织能力演化相互交织而逐步形成的。 本研究的理论贡献主要体现在三个方面：一是揭示了服务型中小企业人工智能技术落地的动态演化过程，推动相关研究由静态采纳视角转向过程展开视角；二是细化了动态能力在中小企业人工智能技术落地情境中的具体展开方式，增强了动态能力视角的情境解释力；三是提出了一个用于理解中小企业人工智能技术落地的解释性路径框架，为理解资源受限情境下人工智能应用实施、能力转化与增长逻辑重构提供了更具解释力的分析框架。在实践层面，本研究为中小企业从现实业务问题出发推进人工智能应用、围绕关键场景培育内部运行能力，并在商业化过程中重视市场适配与交付深化提供了参考。
英文摘要：	The continuous advancement of next-generation artificial intelligence (AI) technologies is accelerating the transition of human society from the era of the digital economy to that of the intelligent economy. As AI technologies continue to evolve and increasingly penetrate business operations, how service-oriented small and medium-sized enterprises (SMEs) implement AI technologies in real business scenarios such as customer interaction, service delivery, and operations management has become an important issue in digital transformation research. Compared with large enterprises, SMEs are generally constrained by limited capital, talent, technological accumulation, and organizational systems. At the same time, they are characterized by shorter decision-making chains, greater organizational flexibility, and relatively lower costs of scenario-based experimentation. Therefore, the process of AI technology implementation in SMEs exhibits distinctive contextual and processual features. Moreover, the value creation of service-oriented enterprises relies heavily on customer interaction, service processes, and delivery experience. As a result, AI technology implementation in such enterprises involves not only the use of technological tools, but also adjustments to service scenarios, division of labor, and customer operation methods. Existing studies have mainly focused on the antecedents, implementation barriers, and outcomes of AI adoption in SMEs. However, there remains a lack of systematic processual explanations of how firms, in real business contexts, gradually transform AI from an external technological change into internal operational capabilities, further develop external application capabilities, and reconstruct their logic of growth. This study takes service-oriented SME X as the research case and examines the formation process, stage transition logic, and overall path mechanism of AI technology implementation in SMEs from the perspective of dynamic capabilities. In addition, the entrepreneur’s judgment and driving role at critical junctures are incorporated as an auxiliary explanatory thread. This study adopts a retrospective single-case study method and combines it with a longitudinal process analysis approach. Drawing on multiple sources of data, including formal interviews, informal interviews, publicly available corporate materials, third-party platform information, and internal analytical documents, this study reconstructs the evolutionary process of AI technology implementation in firm X through critical event identification, stage division, open coding, constant comparison, and cross-stage integrative analysis. On this basis, a Gioia data structure and an interpretive path framework are developed. The findings show that the AI technology implementation process of firm X went through four stages: foundation building and path preparation, AI introduction and R&D exploration, AI application implementation and operational system restructuring, and external expansion of AI capabilities. Overall, AI technology implementation in service-oriented SMEs is not a one-time adoption behavior centered on a single technological tool. Rather, it is a dynamic evolutionary path triggered by the obstruction of prior development paths, advanced through the formation of internal operational capabilities, and continuously calibrated through market validation. More specifically, first, AI technology implementation in service-oriented SMEs is not a linear process of technological application, but unfolds gradually through the joint effects of problem re-evaluation, opportunity construction, strategic commitment, path convergence, breakthroughs in key scenarios, and external application. Second, under the pressure of prior path constraints and the narrowing of existing adjustment space, the firm gradually constructed AI as a new development opportunity from an external technological phenomenon and formed sustained strategic actions under the joint influence of resource constraints and existing organizational foundations. Third, AI technology implementation is a continuous transformation process from technological application to the formation of internal operational capabilities, and further from internal operational capabilities to external application capabilities and the reconstruction of growth logic. In this process, the key capabilities developed by the firm are mainly reflected in contextual sensing and opportunity recognition capability, strategic commitment and resource integration capability, and scenario embedding and system restructuring capability. These three capabilities continuously drive the formation and adjustment of the overall implementation path across different stages. This study constructs an interpretive path framework for AI technology implementation in service-oriented SMEs and conceptualizes it as a dynamic evolutionary process consisting of “foundation activation, opportunity sensemaking, strategic commitment and path convergence, key-scenario embedding and the formation of internal operational capabilities, and external application and the reconstruction of growth logic.” The findings indicate that AI technology implementation in SMEs is gradually formed under the joint effects of real-world pressures, resource constraints, key-scenario traction, and continuous market validation, through the intertwining of entrepreneurial agency and organizational capability evolution. The theoretical contributions of this study are threefold. First, it reveals the dynamic evolutionary process of AI technology implementation in service-oriented SMEs and advances related research from a static adoption perspective to a process-oriented perspective. Second, it specifies how dynamic capabilities unfold in the context of AI technology implementation in SMEs, thereby enhancing the contextual explanatory power of the dynamic capabilities perspective. Third, it proposes an interpretive path framework for understanding AI technology implementation in SMEs, providing a more explanatory analytical framework for understanding AI application implementation, capability transformation, and growth logic reconstruction in resource-constrained contexts. In practical terms, this study provides implications for SMEs to promote AI application based on real business problems, cultivate internal operational capabilities around key scenarios, and attach importance to market adaptation and delivery deepening during the commercialization process.
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