Author: Hyunhwan Aiden Lee

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The Effects of Virtual AI Influencers’ Form Realism on Consumers Perception and Behavior on Social Media

Inhwa Kim, Chung-Wha ‘Chloe’ Ki, Hyunhwan “Aiden” Lee, and Youn-Kyung Kim

The existing literature examines VIs’ utilitarian, personal, and relational traits, but a gap exists in interface design understanding. This study employs avatar marketing and ambivalence theories to explore how VIs’ design elements (VI’s varying form realism and behavioral realism design) induce ambivalent emotions (eeriness and coolness) and impact VI marketing performance (follow and purchase intentions). Two experimental studies were conducted, using ANCOVA, multiple regression, and PROCESS macro analyses. In Study 1, utilizing contemporary VIs from the market, it was revealed that a VI exhibiting mid (vs. low) form realism triggered heightened perceptions of both eeriness and coolness, with these effects being magnified under the condition of high (vs. low) behavioral realism. In Study 2, using VIs generated through the StyleGAN AI technique, it was validated that those with high (vs. low) form realism were associated with decreased perceptions of eeriness and increased coolness, particularly when combined with high (vs. low) behavioral realism. Additionally, our findings emphasized the negative impact of eeriness and the positive influence of coolness on VI performance outcomes. In summary, our study reveals the significant role of VI interface designs in VI marketing performance, highlighting consumers’ mixed emotions as crucial mediators in this context. Additionally, our findings highlight the negative impact of eeriness and the positive influence of coolness on VI performance. Its primary contribution is uncovering VIs’ interface designs as significant factors in VI marketing performance, while recognizing consumers’ ambivalent emotions as vital mediators in this relationship.
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Clothes Made of Pixels and Bits: An AI-driven Topic Modelling Analysis of Fashion NFTs Compared to Digital Fashion

Chenn, Ashley Stevens, Hyunhwan Aiden Lee, Sze Man Chong, Juyeun Jang, and Chung-Wha Chloe Ki. ” Clothes Made of Pixels and Bits: An AI-driven Topic Modelling Analysis of Fashion NFTs Compared to Digital Fashion.”

Non-fungible tokens (NFTs) exploded onto the global digital landscape in 2020, spurred by pandemic-related lockdowns and government stimulus (Ossinger, 2021). An NFT is a unit of data stored on a blockchain that represents or authenticates digital or physical items (Nadini, 2021). Since it resides on a blockchain, NFTs carry the benefits of decentralization, anti-tampering, and traceability (Joy et al., 2022). Fashion brands quickly capitalized on these features, launching fashion NFT collections and garnering significant profits from the sale of fashion NFTs in 2021 (Zhao, 2021). For example, Nike’s December 2021 acquisition of RTFKT (pronounced “artifact”) resulted in USD 185 million in sales less than a year after their acquisition (Marr, 2022).
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A New Auditory Image for Social Media: Moving towards Correlation of Spectrographic Analysis and Interpretation with Audience Perception

Nguyen Le Thanh, Hyunhwan “Aiden” Lee, Joseph Johnson, Mitsunori Ogihara, Gang Ren, and James W. Beauchamp. (2019). “A new auditory image for social media: Moving towards correlation of spectrographic analysis and interpretation with audience perception”

Spectrogram and other time-frequency analysis methods transfer an audio file into an auditory image. When signal processing-based analysis and interpretation is performed on these auditory images instead of an audio signal, spectrographic analyses can identify interesting patterns that focus on very different aspects of the signal compared to an audio-based analysis. To facilitate an auditory image-based study, a quantitative analysis and interpretation framework is implemented for exploring the spectrographic images in multiple time and frequency scales and for automatically identifying image features that are relevant to human auditory perception. This analysis framework is applied to two social media datasets: (1) soundtracks from video commercials and “hit” music excerpts from social media platforms, and (2) soundtracks from television and film. Analysis results from social media are also compared with audience subjective evaluations to validate the perceptual relevance of the identified spectrographic patterns.
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Multi-Scale Auralization for Multimedia Analytical Feature Interaction

Nguyen Le Thanh, Hyunhwan “Aiden” Lee, Joseph Johnson, Mitsunori Ogihara, Gang Ren, and James W. Beauchamp, “Multi-Scale Auralization for Multimedia Analytical Feature Interaction”

Modern human-computer interaction systems use multiple perceptual dimensions to enhance intuition and efficiency of the user by improving their situational awareness. A signal processing and interaction framework is proposed for auralizing signal patterns for augmenting the visualization-focused analysis tasks of social media content analysis and annotations, with the goal of assisting the user in analyzing, retrieving, and organizing relevant information for marketing research. Audio signals are generated from video/audio signal patterns as an auralization framework, for example, using the audio frequency modulation that follows the magnitude contours of video color saturation. The integration of visual and aural presentations will benefit the user interactions by reducing the fatigue level and sharping the users’ sensitivity, thereby improving work efficiency, confidence, and satisfaction.
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DEEPSENSE: A Deep-Learning Predictive Tool for Evaluating Effectiveness of Video Commercials

Joseph Johnson, Gang Ren, Mitsunori Ogihara, and Hyunhwan “Aiden” Lee, “DEEPSENSE: A Deep-Learning Predictive Tool for Evaluating Effectiveness of Video Commercials,”

  • accepted for Theory + Practice in Marketing Conference 2019
  • Revising manuscript for Journal of Marketing.
  • Artificial Intelligence in Management Conference, University of Southern California. March 14, 2020

Testing and validation of television commercials require sophisticated audience response collection schemes at various production and deployment stages of a commercial’s lifecycle including script writing, actor casting and footage shooting. Audience response at these stages allows interactive adjustment to the production and deployment processes which increases the effectiveness of the television commercials. Current testing advertising approaches include manual annotation, focus group studies and audience/customer opinion surveys. Besides their high operational cost, these approaches for collecting audience response data are usually time-consuming and lack immediacy.

We propose a computational “media cognition” tool that uses deep neural networks on video, audio and natural language content of commercials as training data and the audience response attributes as the training labels to form a predictive analysis framework for video commercials. Our data source is YouTube video commercials. The audience response data include the manual annotations as well as YouTube likes/dislikes and comments.

We find that the direct content modeling system performs better when we combine the video, audio, and text dimensions of the content than employing any of these dimensions alone. For predictive analysis based on a single dimension, the video stream has the highest predictive power. The central implication of our study is that firms can train deep learning algorithms directly on the content of existing commercials and use them to reliably predict audience response of unreleased media content.

Keywords: deep learning, advertising effectiveness, multimedia content analysis, long short-term memory, convolutional neural network

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Multi-Dimensional Spatio-Temporal Video Contents Analysis (Dissertation Essay 2)

Hyunhwan “Aiden” Lee, “Multi-Dimensional Spatio-Temporal Video Contents Analysis” (Dissertation)

  • Dissertation Committee
    • Joseph Johnson (University of Miami, Chair)
    • Gerard J. Tellis (University of Southern California)
    • A. “Parsu” Parasuraman (University of Miami)
    • Oded Netzer (Columbia University)
    • Ogihara Mitsunori (Computer Science, University of Miami)
  • Proposal defended, May 3, 2019. Coral Gables, FL.
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Geo-Influence: Modeling Location-Specific Effects of Social Influence on Brand Preferences

Hyunhwan “Aiden” Lee, Joseph Johnson, and Gerard J. Tellis. “Geo-Influence: Modeling Location-Specific Effects of Social Influence on Brand Preferences”

http://geoinfluence.net

  • Revising manuscript for Journal of Marketing Research
  • The 41st ISMS Marketing Science Conference, University of Rome. June 20 – 22, 2019
  • The 2019 Haring Symposium, Indiana University. April 19 – 20, 2019
  • The 1st Interdisciplinary Research Cluster Day, University of Miami. April 1, 2019
  • The 3rd Annual Three Minute Thesis Competition, University of Miami. February 6, 2019
  • Behavioral Insights from Text Conference, University of Pennsylvania. January 17, 2020
  • Artificial Intelligence in Management Conference, University of Southern California. March 14, 2020
No brand is the market leader uniformly across geographic locations. Brands have strong and weak markets. Therefore, analysts need to track brand preferences by location. However, current methods of tracking geographic brand preferences (e.g., surveys, company sales, scanner panel data, Google Analytics, etc.) have limitations: limited sampling of brands, stores, and categories with time and region aggregation. We address these limitations through a spatial, nonparametric approach to track brand preferences at the micro-temporal and micro-geographic level using Twitter. Our approach inherits the intrinsic strengths of social media: micro-temporal, micro-geographic, faster than news media, higher frequency than sales, low cost, competitive monitoring, sentiment inclusion, and social network inclusion. A key feature of our approach is modeling the temporal geographic influence of social connections based on the followers of each post. We estimate our model for carbonated soft drinks brands. Our validation uses Pepsi sales, an apparel fashion brand, and the 2016 U.S. presidential election (a domain where marketing is critical). We find that geographic influence adds predictive power to micro-temporal and micro-geographic brand measures, leads online sales and predicts elections. Our study implies that the micro-temporal and micro-geographic social media measures provide actionable insights for marketing brands and political candidates.

Keywords: micro-temporal brand preference, micro-geographic brand preference, sentiment analysis, Twitter, geographic influence model, election prediction
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Brand Hazard: A Prognostic Complement to Customer-Based Brand Equity

Joseph Johnson, Debanjan Mitra, Sivaramakrishnan Siddarth, Hyunhwan “Aiden” Lee

  • Revision invited, Journal of Marketing

Existing customer-based brand equity metrics are built on customers’ choices and preferences and not on the dynamics inherent in their brand switching and timing, which potentially handicaps the prognostic ability of such metrics.   To remedy this we develop a complementary metric that focuses on the rate of change in customers’ purchase transitions. We use the counting process to model the repeat and switching rates of a brand, parse out baselines for each transition rate, cumulate these baselines over a given duration, and combine them to obtain brand hazard (BH). Using customer panel data, we estimate BH for six leading brands in three product categories over sixteen two-year moving windows.  We find increases in BH foretells future declines in the intercept of logit models of customers’ brand choice (BCI) – a customer based brand equity metric estimated from the same data. More important, we find that changes in BH and BCI are able to predict future changes in a brand’s price premium and revenue premium in the broader market.  Our findings imply that BH complements BCI, and tracking them together provides a comprehensive prognosis of a brand’s health.

Key Words:  Branding, Brand Equity, Brand Management, Hazard Models, Markov Process, Counting process