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论文编号：	15509
作者编号：	2120233695
上传时间：	2025/6/13 19:20:56
中文题目：	短视频广告视觉动态性对消费者观看兴趣的影响研究
英文题目：	Research on the Influence of Visual Dynamism on Consumer Viewing Interest in Short-form Video Advertising
指导老师：	任星耀
中文关键字：	短视频广告；视觉动态性；观看兴趣；音画节奏匹配度；视觉复杂度
英文关键字：	Short-form Video Advertising，Visual Dynamism，Viewing Interest，Audio-Visual Rhythm Congruence，Visual Complexity
中文摘要：	随着互联网的蓬勃发展，媒介形态经历了从图文单向传播到用户深度参与的转变，短视频成为当前具有重大影响力的主流媒介形式。基于此，广告主可以通过短视频广告这一新兴广告形式触达庞大数量的消费者，短视频广告市场规模和预算投入持续增加。然而在数字化、碎片化时代，消费者的注意力已成为一种稀缺资源，在广告数量激增、内容同质化、视觉审美疲劳以及用户注意力跨度缩短等多重挑战下，如何科学化提高短视频有效性，及如何进行视频创意设计使短视频内容能在短时间内脱颖而出，吸引消费者注意力，已成为短视频广告研究领域和营销实践中平台商、广告主和广告制作公司面临的重要问题。 本文首先通过对视频广告进行文献梳理，发现尽管在视频广告效果领域已积累了丰富文献，但大多研究聚焦于广告态度、购买等偏向营销漏斗后端的传统效果指标，观看兴趣这一能衡量营销漏斗前端效果的指标，尚未在短视频广告中被探讨。其次，通过对视频广告效果影响因素的梳理发现，现有研究尚未挖掘短视频广告在视觉呈现方面的显著独特性——视觉动态性，以及尚未关注其对广告效果的影响。最后，通过梳理现有学者对视觉动态性的研究，发现存在局限与不足：第一，关于视觉动态性对广告效果的影响研究结论不一致；第二，对视觉动态性的测量主要使用人工编码量化，且测量对象多为静态图像；第三，虽然已经有以视频为测量对象的研究，但多停留于影响动态感知的前因变量，而非真正意义的整体视觉动态性。因此，本研究聚焦短视频广告中的视觉动态性，采用计算机视觉技术与深度学习新方法对视频的整体视觉动态性进行量化测量，探究其对观看兴趣的影响。此外，本研究引入了音画节奏匹配度和视觉复杂度作为调节变量，旨在探讨不同视频创意元素作用下视觉动态性的影响变化。 在文献综述的基础上，本文提出研究假设并构建了相应的理论模型。为验证所提出的假设，本研究采用了实证分析的方法，对来自一家大型社交媒体平台公司的用户观看广告数据进行分析。数据分析结果表明，短视频广告视觉动态性和消费者观看兴趣呈倒U形关系，低视觉动态性到中等视觉动态性，观看兴趣升高，而中等视觉动态性到高视觉动态性，观看兴趣降低。即与低水平或者高水平的动态性相比，中等水平的动态性更有利于增加消费者对短视频广告的观看兴趣。此外，当音画节奏匹配度高（vs低）时，视觉动态性对短视频广告观看兴趣的倒U形关系增强；当视觉复杂度高（vs低）时，视觉动态性对短视频广告观看兴趣的倒U形关系会减弱。本文的研究发现为视频创作者和广告制作商提供了视觉呈现数字创意和内容素材优化指导，同时对短视频平台商和广告主制定有针对性的广告推荐和投放策略也具有重要实践意义。
英文摘要：	With the rapid development of the internet, media formats have evolved from unidirectional graphic-text communication to user-immersive engagement, establishing short-form video advertising as a dominant medium with substantial influence. This emerging format enables advertisers to reach massive consumer audiences through escalating budget allocations. However, in the digitalized and fragmented media era where consumer attention constitutes a scarce resource, key challenges including advertising saturation, content homogenization, visual fatigue, and shrinking attention spans necessitate scientific approaches to enhance short-form video advertising effectiveness. The critical question of optimizing visual creativity to achieve immediate consumer engagement through breakthrough content design has become paramount for platforms, advertisers, and agencies in both academic research and marketing practice. This study first reviews extant literature on video advertising effects, revealing that while substantial research exists on traditional downstream metrics like ad attitude and purchase intention in the marketing funnel, viewing interest - a crucial front-end engagement indicator - remains underexplored in short-form video advertising. Furthermore, current research inadequately addresses the distinctive visual dynamism characteristic of short-form video advertising's visual presentation and its impact on advertising effectiveness. Existing limitations in visual dynamism research include inconsistent findings regarding its effects, reliance on manual coding for static image measurement, and insufficient operationalization of holistic video-level dynamism. Addressing these gaps, this investigation employs computer vision and deep learning methodologies to quantify holistic visual dynamism in short-form video advertising and examines its non-linear impact on viewing interest. The study further introduces audio-visual rhythm congruence and visual complexity as moderators to explore contingent effects of visual creative elements. Building on theoretical foundations, this research establishes hypotheses and constructs a conceptual framework. Empirical analysis utilizing behavioral data from a major social media platform's advertising ecosystem reveals three key findings: an inverted U-shaped relationship between visual dynamism and viewing interest, where moderate dynamism levels optimize engagement compared to low or high extremes; enhanced audio-visual rhythm congruence strengthens the curvilinear relationship; elevated visual complexity attenuates the inverted U-shaped effect. These findings advance video advertising literature by introducing computational measurement of visual dynamism and establishing boundary conditions through multimedia congruence mechanisms. Practically, the study provides actionable guidelines for content creators in visual storytelling optimization and informs platform-level advertising recommendation algorithms through dynamic creative adjustment strategies.
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