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论文编号：	14839
作者编号：	1120180946
上传时间：	2024/6/26 12:25:28
中文题目：	用户对人脸识别的技术接受与 产品选择研究
英文题目：	Research on User Acceptance and Product Selection of Facial Recognition Technology
指导老师：	李东进
中文关键字：	人脸识别技术；合理性策略；技术接受；风险情境；技术模式；产品选择
英文关键字：	Face recognition technology；Legitimacy strategies；Technology acceptance；Risk scenarios；Technical modes；Product selection
中文摘要：	作为人工智能技术发展的关键支撑与重要保障，人脸识别等生物特征识别技术的发展受到了广泛关注。近年来，借助于计算机视觉、深度学习、传感技术等领域的进步，人脸识别的算法不断更新和发展，各种人脸识别的应用也层出不穷。然而，用户对人脸识别技术的态度却是矛盾的：一方面，人脸识别的高效性和便捷性激发了人们对这种新型身份验证技术的浓厚兴趣。另一方面，不断涌现的人脸识别负面事件也让他们对这项创新所潜藏的风险感到担忧。因此，如何提高用户对人脸识别的技术接受与产品选择意愿成为生物特征识别甚至人工智能发展中亟待解决的问题。 先前有关人脸识别技术接受的研究大多局限于采用经典的技术接受模型或从隐私安全的角度进行分析，面对用户对这种全新的生物特征识别技术的巨大担忧甚至强烈抵触，仅从上述两个方面展开讨论难以形成对其技术接受与产品选择的全面理解。鉴于此，首先，本研究从制度理论的视角出发，开发了人脸识别技术的合理性策略；其次，探讨了相关合理性策略促进技术接受的机制与边界条件；最后，结合人脸识别技术应用的具体场景，讨论了风险情境与人脸识别模式的交互效应对人脸识别产品选择意愿的影响。总体而言，本研究取得了较为丰富的研究结论。 第一，本研究根据扎根理论的研究范式开发了人脸识别技术的合理性策略，并通过实证检验的方式对相关合理性策略的结构维度进行了验证，最终确定人脸识别合理性策略由技术实用性、技术道德性和技术认知性3个维度构成，这3个维度的贡献权重由大到小依次为技术实用性、技术认知性和技术道德性。其中，技术实用性由自身利益提升性与公众利益关注性2个子维度构成，技术道德性由负面结果鲜少性和流程结构合理性2个子维度构成，技术认知性由相关知识了解性、社会影响深刻性与发展前景认同性3个子维度构成。因此，人脸识别合理性策略是由技术实用性、技术道德性与技术认知性3个维度7因子构成的多维度位阶结构，并由此开发了相关量表。 第二，本研究探讨了人脸识别技术合理性策略促进技术接受的机制与边界条件，并通过实证检验的方式对相关机制与边界条件的影响进行了验证。本研究假设，人脸识别合理性策略通过促进技术信任而影响技术接受，用户的公共自我意识与感知监管严格性在这一过程中起到调节作用：用户公共自我意识可能会负向调节技术实用性、技术道德性与技术认知性对技术信任的积极影响；而感知监管严格性可能会正向调节技术实用性、技术认知性和技术道德性对技术信任的积极影响。实证检验结果证实了上述大部分假设，并发现，人脸识别技术的合理性策略对技术信任的影响从大到小依次是技术道德性、技术实用性和技术认知性。而感知监管严格性对于技术道德性对技术信任影响过程的调节作用并不显著。 第三，本研究提出了风险情境与人脸识别模式的交互效应对人脸识别产品选择意愿影响的相关假设与研究模型，并通过实验的方式进行了检验。本研究认为，在低风险情境下，相较于不见脸的人脸识别产品，当人们在人脸识别的过程中面对自己的脸时，由自我关注引发的消极偏差、对个人容貌的过度苛刻、拍摄效果与本人之间的差异、拍摄过程的尴尬体验和对隐私的关注都会引发不良的使用体验，此时，人们对不见脸模式的人脸识别产品选择意愿更高。而在高风情境下，人们更加担忧出现意外的结果，他们希望能得到更多的确定性与控制感，而不见脸模式人脸识别产品不显示面部图像的特点恰恰增加了模糊性与不确定性感知，此时，人们对见脸模式人脸识别产品选择意愿更高。实验结果证实了上述假设，并发现感知焦虑在这一过程中起到中介作用。 通过对上述问题的探索，本研究构建了用户对人脸识别技术接受与产品选择的整体性框架。从理论上来说，研究丰富了对人脸识别技术合理性策略结构内涵的认识，拓宽了制度理论的适用性，加强了对相关合理性策略促进技术接受过程的理解，深化了对人脸识别技术应用情境与技术模式匹配性的思考。从实践上来说，研究结论不仅提示相关企业及组织对人脸识别技术的推广要与更广泛的社会规则、规范和惯例互动，通过一系列超越传统实用性推广的策略将人脸识别技术嵌入受众群体共享的社会信仰体系、道德标准和文化习俗中；研究还强调了对人脸识别技术使用情境的仔细区分以及深入理解用户心理的重要性。通过使用情境与技术模式之间的良好匹配，可以提升用户对人脸识别产品的使用体验，增强用户对人脸识别产品的选择意愿与满意程度。
英文摘要：	As a fundamental pillar and crucial underpinning for the advancement of artificial intelligence, biometric recognition technologies, especially face recognition, have garnered significant attention. In recent years, aided by advancements in computer vision, deep learning, sensing technology, and more, face recognition algorithms have undergone continuous refinement and evolution. This has led to a proliferation of applications in the user market. However, user perceptions of face recognition technology remain ambivalent. While the efficiency and convenience offered by face recognition have aroused widespread interest in this novel identity verification method, recurrent negative incidents associated with it have also heightened concerns regarding the potential risks embedded within this innovation. Consequently, enhancing user acceptance of facial recognition technology and their willingness to adopt related products has become a pressing issue in biometric identification and the broader field of artificial intelligence. Prior research on the acceptance of face recognition technology has predominantly centered on classical technology acceptance models or has approached the topic from the vantage point of privacy and security. Given the pronounced concerns and even resistance from users regarding this nascent biometric recognition technology, a comprehensive understanding of technology acceptance cannot be attained solely by examining the aforementioned perspectives. In light of this, the present study first formulates legitimacy strategies rooted in the institutional theory to enhance technology acceptance. Subsequently, it investigates the mechanisms and boundary conditions underpinning these legitimacy strategies in fostering technology acceptance. Furthermore, by considering specific application scenarios of face recognition technology, this study elucidates the interactive effects between risk scenarios and face recognition modes on user preference for facial recognition products. In sum, this research offers substantive insights. To begin with, based on the research paradigm of grounded theory, this study develops legitimacy strategies for face recognition technology and validates the structural dimensions of these strategies through empirical testing. Ultimately, it is determined that the legitimacy strategies for face recognition technology consist of three dimensions: technology pragmatism, technology morality, and technology cognition. The contribution weights of these three dimensions, from highest to lowest, are technology pragmatism, technology cognition, and technology morality. Among them, technology pragmatism consist of two sub-dimensions: enhancement of personal interests and attention to public benefits. Technology morality consist of two sub-dimensions: rarity of negative consequences and rationality of structural processes. Technology cognition consist of three sub-dimensions: comprehension of relevant knowledge, profundity of social impact, and identification with development prospects. Therefore, the legitimacy strategies for face recognition technology form a multidimensional hierarchical structure consisting of three dimensions and seven factors. From this structure, pertinent scales are formulated. Additionally, this study delves into the mediating mechanisms and boundary conditions under which legitimacy strategies enhance acceptance of facial recognition technology. To investigate the effects of these mechanisms and conditions, empirical tests are conducted. It is hypothesized that legitimacy strategies for face recognition influence technology acceptance by bolstering technology trust. Moreover, user public self-awareness and perceived regulatory stringency might serve as moderators in this relationship. Specifically, while user self-awareness could dampen the positive influence of legitimacy strategies on trust, perceived regulation stringency might amplify it. These assumptions are supported by empirical test results. Data analysis reveals the following impact ranking of legitimacy strategies on trust: technology morality, technology pragmatism, and technology cognition. Nonetheless, perceived regulatory stringency doesn't significantly moderate the influence of technology morality on trust. Subsequently, we propose hypotheses and introduce a research model exploring the interplay between risk scenarios and modes of face recognition concerning users' product preferences. The hypotheses were evaluated through experiments. Results indicate that in low-risk situations, users prefer face recognition products that don't display their face. This preference arises from potential biases like self-focus, over-criticism of one's appearance, image discrepancies, discomfort during capture, and privacy worries. Conversely, in high-risk scenarios, due to their desire for certainty and control, users lean towards products that visibly display faces, perceiving the face-less mode as ambiguous and uncertain. Experimental data confirms these hypotheses, highlighting perceived anxiety as a mediator in this decision-making process. Drawing upon the aforementioned discussions, this study establishes a comprehensive framework for user acceptance and product selection of facial recognition technology. Theoretically, this study deepens understanding of the structural aspects of legitimacy strategies for face recognition technology, broadens the applicability of institutional theory, elucidates how face recognition legitimacy strategies promote technology acceptance, and spurs thinking about the alignment of application scenarios with technical modes of face recognition. Practically, the research conclusions suggest that the advancement of face recognition technology by relevant enterprises and organizations must resonate with broader societal rules, norms, and practices. Moreover, strategies beyond traditional pragmatic promotion should embed face recognition technology into the social belief systems, moral standards, and cultural customs of the target audiences. The study also highlights the importance of distinguishing the use scenarios for face recognition technology and understanding user psychology. By ensuring a favorable alignment between situational contexts and technical modes, it is possible to enhance user experience with facial recognition products, thereby increasing user preference for facial recognition products and their satisfaction.
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