Yuan, Lin, Gene Moo Lee, Hao Xia, Qiang Ye. “From Spacing to Scheduling: The Effect of Seller Posting Time Strategies for Short Video Advertising”, Accepted at CIST 2023.
We examine how a seller’s posting time strategies of short video advertising affect consumer engagement and product sales. Drawing on the two-factory theory, we develop hypotheses on the effects of ad spacing and scheduling on ad effectiveness. The empirical results based on a unique short video ad dataset from Douyin, the Chinese counterpart of TikTok, indicate that there exists an inverted U-shape relationship between ad spacing and sales. Also, certain posting times significantly increase ad effectiveness. Interestingly, the effects of posting time strategies manifest under specific conditions: the effects of ad spacing on consumer purchase are strengthened for products with higher discounts, while this moderating effect is diverse for different posting times. These results provide nuanced insights to help ad managers make strategic decisions on ad posting times in the important context of social commerce.
Park, Jaecheol, Myunghwan Lee, Gene Moo Lee “Mobile Resilience: The Effect of Mobile Device Management on Firm Performance during the COVID-19 Pandemic”, Work-in-Progress.
Based on an industry collaboration
Presentations: UBC 2023, MSISR 2023, KrAIS 2023, WeB 2023
The use of mobile information technology (IT) has become increasingly vital for businesses, especially for remote and hybrid work during the COVID-19 pandemic, providing employees with accessibility, flexibility, and responsiveness. However, despite its growing significance, the business value of mobile device management and its role in establishing digital resilience during crises remain underexplored in the literature. To address this research gap, our study examines the effect of mobile device management on a firm’s resilience to external shocks. Using a proprietary dataset from a global mobile device management solution provider for public U.S. firms over the three-year period of 2019-2021, we find that firms with mobile device management have a better financial performance during the pandemic, demonstrating greater resilience to the shock. Furthermore, we observe heterogeneous resilience effects across industries, with greater impacts in non-high-tech industries than in high-tech ones, and in manufacturing, retail, and service industries compared to others. Our findings are robust to various tests. This study contributes to the literature by emphasizing the crucial role of mobile device management in building digital resilience.
Given the importance of exploratory innovation in fostering firms’ sustainable competitive advantages, firms often depend on technological assets or inter-firm relationships to pursue exploration. Regarded as a general-purpose technology, deep learning (DL)-based artificial intelligence (AI) can be an exploratory innovation-seeking instrument for firms in searching unexplored resources and thereby broadening their boundary. Drawing on the theories of organizational learning and path dependence, we hypothesize the impact of a firm’s DL capabilities on exploratory innovation and how DL capabilities interact with conventional pathbreaking activities such as technical assets and inter-firm relationships. Our empirical investigations, based on a novel DL capabilities measure constructed from comprehensive datasets on AI conferences and patents, show that DL capabilities have positive impacts on exploratory innovation. The results also show that extant technological assets (i.e., structured data management capabilities) and inter-firm relationships remedy the constraints on a firm’s innovation-seeking behaviors and that these path-breaking activities negatively moderate the positive impact of DL capabilities on exploratory innovation. To our knowledge, this is the first large-scale empirical study to investigate how DL affects exploratory innovation, contributing to the emerging literature on AI and innovation.
Zhang, Xiaoke, Mi Zhou, Gene Moo Lee (2022) “Generative AI and Creator Economy: Investigating the Effects of AI-Generated Voice on Online Video Creation”, Preparing for resubmission to Management Science.
The rising demand for online video content has fostered one of the fastest-growing markets as evidenced by the popularity of platforms like TikTok. Because video content is often difficult to create, platforms have attempted to leverage recent advancements of artificial intelligence (AI) to help creators with their video creation process. However, surprisingly little is known about the effects of AI on content creators’ productivity and creative patterns in this emerging market. Our paper investigates the adoption impact of AI-generated voice – a generative AI technology creating acoustic artifacts – on video creators by empirically analyzing a unique dataset of 4,021 creators and their 428,918 videos on TikTok. Utilizing multiple audio and video analytics algorithms, we detect the adoption of AI voice from the massive video data and generate rich measurements for each video to quantify its characteristics. We then estimate the effects of AI voice using a difference-in-differences model coupled with look-ahead propensity score matching. Our results suggest that the adoption of AI voice increases creators’ video production and that it induces creators to produce shorter videos with more negative words. Interestingly, creators produce more novel videos with less self-disclosure when using AI voice. We also find that AI-voice videos received less viewer engagement unintendedly. Our paper provides the first empirical evidence of how generative AI reshapes video content creation on online platforms, which provides important implications for creators, platforms, and policymakers in the digital economy.
Contrary to the promise that AI will transform various industries, there are conflicting views on the impact of AI on firm performance. We argue that existing AI capability measures have two major limitations, limiting our understanding of the impact of AI in business. First, existing measures on AI capability do not distinguish between stated strategies and actual AI implementations. To distinguish stated AI strategy and actual AI capability, we collect various AI-related data sources, including AI conferences (e.g., NeurIPS, ICML, ICLR), patent filings (USPTO), inter-firm transactions related to AI adoption (FactSet), and AI strategies stated in 10-K annual reports. Second, while prior studies identified successful AI implementation factors (e.g., data integrity and intelligence augmentation) in a general context, little is known about the relationship between AI capabilities and in-depth innovation performance. We draw on the neo-institutional theory to articulate the firm-level AI strategies and construct a fine-grained AI capability measure that captures the unique characteristics of AI-strategy. Using our newly proposed AI capability measure and a novel dataset, we will study the impact of AI on firm innovation, contributing to the nascent literature on managing AI.
The rapid growth in e-commerce has led to a concomitant increase in consumers’ reliance on digital word-of-mouth to inform their choices. As such, there is an increasing incentive for sellers to solicit reviews for their products. Recent studies have examined the direct effect of receiving incentives or introducing incentive policy on review writing behavior. However, since incentivized reviews are often only a small proportion of the overall reviews on a platform, it is important to understand whether their presence on the platform has spillover effects on the unincentivized reviews which are often in the majority. Using the state-of-the-art language model, Bidirectional Encoder Representations from Transformers (BERT) to identify incentivized reviews, a document embedding method, Doc2Vec to create matched pairs of Amazon and non-Amazon branded products, and a natural experiment caused by a policy change on Amazon.com in October 2016, we conduct a difference-in-differences analysis to identify the spillover effects of banning incentivized reviews on unincentivized reviews. Our results suggest that there are positive spillover effects of the ban on the review sentiment, length, helpfulness, and frequency, suggesting that the policy stimulates more reviews in the short-run and more positive, lengthy, and helpful reviews in the long run. Thus, we find that the presence of incentivized reviews on the platform poisons the well of reviews for unincentivized reviews.
With the increasing use of online matching platforms, predicting matching probability between users is crucial for efficient market design. Although previous studies have constructed various visual features to predict matching probability, facial features, which are important in online matching, have not been widely used. We find that deep learning-enabled facial features can significantly enhance the prediction accuracy of a user’s partner preferences from the individual rating prediction analysis in an online dating market. We also build prediction models for each gender and use prior theories to explain different contributing factors of the models. Furthermore, we propose a novel method to visually interpret facial features using the generative adversarial network (GAN). Our work contributes to the literature by providing a framework to develop and interpret facial features to investigate underlying mechanisms in online matching markets. Moreover, matching platforms can predict matching probability more accurately for better market design and recommender systems.
Lee, Myunghwan, Gene Moo Lee, Donghyuk Shin, Sang-Pil Han (2022) “Robots Serve Humans? Understanding the Economic and Societal Impacts of AI Robots in the Service Industry”, Working Paper.
Presented at WITS (2020), KrAIS (2020), UBC (2021), DS (2022)
Research assistants: Raymond Situ, Gallant Tang
Service providers, such as restaurants, have been adopting various robotics technologies to improve operational efficiency and increase customer satisfaction. AI Robotics technologies bring new restaurant experiences to customers by taking orders, cooking, and serving. While the impact of industrial robots has been well documented in the literature, little is known about the impact of customer-facing service robot adoption. To fill this gap, this work-in-progress study aims to analyze the impact of service robot adoption on restaurant service quality using 4,610 restaurants and their online customer reviews. We analyzed the treated effect of robot adoption using a difference-in-differences approach with propensity score and exact matching. Estimation results show that restaurant robot adoption has a positive impact on customer satisfaction, specifically on perceived service quality. This study provides both academic and practical implications on emerging AI robotics techniques.
Schulte-Althoff, Matthias, Daniel Fuerstenau, Gene Moo Lee, Hannes Rothe, Robert Kauffman.“What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups”. Working Paper. [ResearchGate]
Previous title: “A Scaling Perspective on AI startup”
Presented at HICSS 2021 (SITES mini-track), Copenhagen Business School 2021, FU Berlin 2021, University of Cologne 2021, University of Bremen 2021, Humboldt Institute for Internet and Society 2021, WITS 2022
We examine how firm revenue scales with labor for revenue-per-employee (RPE) and is moderated by firm-level AI investment. We compare AI start-ups, in which AI provides a competitive advantage, with digital platforms and service start-ups. We use propensity score matching to explain the scaling of start-ups and find evidence for sublinear scaling intensity for revenue as a function of labor. Our study suggests similar scaling intensities between AI and service start-ups, while platform start-ups produce higher scaling intensities. We show that an increase in employee counts is associated with major revenue increases for platform start-ups, while increases were modest for service and AI start-ups.