Browsing by Author "Kim, Eunah"

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    Consumer Responses to Ads on Digital Video-Sharing Platforms: The Phenomenon of Intentional Ad-Viewing Behavior
    (2022-07) Kim, Eunah
    With the popularity of digital videos, digital video-sharing platforms have been receiving attention as a medium that may surpass traditional TV in terms of viewership and as a powerful medium for advertising. Interestingly, on digital video-sharing platforms such as YouTube, consumers sometimes choose to view rather than skip ads in order to support content creators, even if the ads are not relevant to them and they can easily avoid them by clicking on the ‘skip ad’ button. This is a very unique phenomenon that has been hardly observed in any other media platforms, nor has been examined in prior studies. The purpose of this dissertation is two-fold: (1) to investigate whether and to what extent intentional ad-viewing to support content creators is indeed happening on digital video-sharing platforms; and (2) to explore why and when consumers choose to not skip ads for the sake of content creators.A three-phase study using a multi-method approach was performed. In Phase 1, a preliminary survey was conducted (N = 265) to inform and guide the study design and measurement developments of the next two phases. The results demonstrated that consumers sometimes choose to not skip ads in order to support content creators, which confirms the existence of such a novel ad-viewing behavior. In Phase 2, a series of in-depth interviews were conducted to further probe: (1) the motivations driving such behavior and (2) potential influencing factors (N = 20). The Phase 2 in-depth interviews suggest three different but interrelated motivations driving intentional ad-viewing as a way of supporting content creators: gratitude to content creators, extrinsic helping motivation with the expectation of reciprocity, and intrinsic helping motivation from empathy. In Phase 3, which is the main study of this dissertation, an online survey (N = 499) was conducted to formally address the research question and test the hypotheses posed based on the findings of Phase 1 and Phase 2. The results show that amateur content creators and influencers are more likely to generate intentional ad-viewing to support content creators than are professional creators. While helping motivation was not a significant mediator of the relationship between creator type and intentional ad-viewing to support content creators, it was shown to be another significant antecedent of such ad-viewing behavior. This study contributes to advancing ad avoidance research by establishing the previously unknown phenomenon of intentional ad-viewing to support content creators, and by adopting the perspective of helping behavior that has been hardly used in advertising research. This study also provides important practical implications for advertising practitioners and digital media platform companies: the comparative value of placing ads on digital video-sharing platforms, and the consideration of independent and amateur channels.
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    Data supporting: Automated Object Detection in Mobile Eye-Tracking Research: Comparing Manual Coding with Tag Detection, Shape Detection, Matching, and Machine Learning
    (2024-06-20) Segijn, Claire M.; Menheer, Pernu; Lee, Garim; Kim, Eunah; Olsen, David; Hofelich Mohr, Alicia; segijn@umn.edu; Segijn, Claire
    The goal of the current study is to compare the different methods for automated object detection (i.e., tag detection, shape detection, matching, and machine learning) with manual coding on different types of objects (i.e., static, dynamic, and dynamic with human interaction) and describe the advantages and limitations of each method. We tested the methods in an experiment that utilizes mobile eye tracking because of the importance of attention in communication science and the challenges this type of data poses to analyze different objects because visual parameters are consistently changing within and between participants. Python scripts, processed videos, R scripts, and processed data files are included for each method.