Tag Archives: AI

How Does AI-Generated Voice Affect Online Content Creation? Evidence from TikTok

Zhang, Xiaoke, Mi Zhou, Gene Moo Lee (2022) How Does AI-Generated Voice Affect Online Content Creation? Evidence from TikTokWork-in-progress.

Video is one of the fastest-growing online services offered to consumers. A growing number of people today are participating in video creation and consumption in the digital economy. We study whether and how AI-generated voice affects users’ routine efforts and creative efforts in online video creation. Using a unique dataset of 2,617 creators and 273,244 videos collected from TikTok over a 25-week period, we first exploit deep learning models to detect the adoption of AI-generated voice from massive video data. Then we estimate its treatment effect on creators and viewers using a difference-in-differences model coupled with propensity score matching. We find that AI-generated voice increases creators’ routine effort and creative effort in the short term. While it has a long-lasting effect on improving the efficiency of video creation, AI-generated voice cannot consistently motivate creators to include more information in videos, and might even be detrimental to their creative effort in the long term. Our study provides the first empirical evidence on how AI tools reshape video content creation patterns on online platforms, which carries important managerial implications for individual creators, platforms, and policymakers in the digital economy.

Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance

Lee, Myunghwan, Gene Moo Lee (2022) “Ideas are Easy but Execution is Everything: Measuring the Impact of Stated AI Strategies and Capability on Firm Innovation Performance”Work-in-Progress.

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.

Papers on Automation and Robotics

Last update: Aug 23, 2022

In this post, I am gathering robotics-related papers in information systems and related disciplines. This is by no means an exhaustive list. I will keep updating this list.

  1. Park, Jiyong, Jongho Kim (2022) A Data-Driven Exploration of the Race between Human Labor and Machines in the 21st Century, Communications of ACM 65(5):79-87.
  2. Koch, Michael, Manuylov Ilya, Marcel Smolka (2021) Robots and Firms, The Economic Journal 131(638):2553-2584.
  3. Ge, Ruyi, Zhiqiang (Eric) Zheng, Xuan Tian, Li Liao (2021) Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending. Information Systems Research 32(3):774-785.
  4. Jain, Hemant, Balaji Padmanabhan, Paul A. Pavlou, T. S. Raghu (2021) Editorial for the Special Section on Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society. Information Systems Research 32(3):675-687.
  5. Berente, Nicholas, Gu, Bin, Recker, Jan, Santhanam, Radhika. (2021) Special Issue Editor’s Comments: Managing Artificial Intelligence. MIS Quarterly (45: 3) pp. 1433-1450.
  6. Dixon, Jay, Bryan Hong, Lynn Wu (2021) The Robot Revolution: Managerial and Employment Consequences for Firms. Management Science 67(9):5586-5605.
  7. Schanke, Scott, Gordon Burtch, Gautam Ray (2021) Estimating the Impact of “Humanizing” Customer Service Chatbots. Information Systems Research 32(3):736-751.
  8. Park, H., Jiang, S., Lee, O. D., Chang, Y. (2021) Exploring the Attractiveness of Service Robots in the Hospitality Industry: Analysis of Online Reviews. Information Systems Frontier
  9. Graetz, G., Michaels, G. 2018. Robots at work. Review of Economics and Statistics (100:5), pp. 753-768.
  10. Luo, Xueming, Siliang Tong, Zheng Fang, Zhe Qu (2019) Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases. Marketing Science 38(6):937-947.

 

Learning Faces to Predict Matching Probability in an Online Dating Market

Kwon, Soonjae, Sung-Hyuk Park, Gene Moo Lee, Dongwon Lee. “Learning Faces to Predict Matching Probability in an Online Dating Market”. Working Paper.

  • Presentations: DS (2021), AIMLBA (2021), WITS (2021), ICIS (2022)
  • Based on an industry collaboration

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.

AI Robot Adoption in the Service Industry (KOSEN Report 2020)

Gene Moo Lee (2020) “AI Robot Adoption in the Service Industry”. KOSEN Report  DOI: https://doi.org/10.22800/kisti.kosenexpert.2020.588

  • This is an industry report on AI robot adoption in the service industry.

Abstract

디지털 전환(Digital Transformation) 시장은 2020년 기준 3,550억 달러의 가치가 있으며, 2027년까지의 연간 성장률은 22.5%에 이를 것으로 예상되고 있다.  스마트폰의 보급과 무선인터넷의 확산은 디지털생태계가 구축될 수 있는 환경을 조성하였으며, 이용자들의 지속적인 디지털콘텐츠 활용으로 인한 데이터의 폭발적인 증가는 방대한 양의 데이터를 효율적으로 처리할 수 있는 빅데이터 처리 기술이 발달할 수 있는 밑거름이 되었다. 뿐만 아니라 사물인터넷(IoT), Quantum 컴퓨팅, 인공지능 기술의 발달은 기존의 오프라인 시장이 디지털 시장으로 전환할 수 있는 촉매제 역할을 하여 디지털 시장이 성장할 수 있는 원동력이 되었다. 실제로 다양한 산업 영역에서 디지털 시장 내에서 새로운 사업 기회를 포착하고자 하는 시도가 많이 이루어지고 있으며, 이를 바탕으로 오프라인에서 벗어나 온라인 디지털 시장에서 다양한 가치 창출을 가능하게 하였다. 전통산업의 디지털 전환이 가속화되고 있음은 다음과 같은 사례를 통해 파악할 수 있다. 자동차산업에서는 자율주행 서비스를 통해 고객들의 주행 데이터를 디지털화하여 무인 자동차 시대를 위한 준비를 하고 있으며, 의료산업에서는 원격진료를 통해 물리적 한계를 뛰어넘는 의료서비스라는 가치를 창조하고 있고, 제조산업에서는 생산시스템 자동화를 통해 생산 효율성을 높이고 품질을 높이는 활동을 하고 있다.

Trustworthy Face? The Effect and Drivers of Comprehensive Trust in Online Job Market Platform

Kwon, Jun Bum, Donghyuk Shin, Gene Moo Lee, Jake An, Sam Hwang (2020) “Trustworthy Face? The Effect and Drivers of Comprehensive Trust in Online Job Market Platform”. Work-in-progress.

The abstract will appear here.

Robots Serve Humans: Does AI Robot Adoption Enhance Operational Efficiency and Customer Experience?

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 IndustryWorking 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.

What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups

Schulte-Althoff, Matthias, Gene Moo Lee, Hannes Rothe, Robert Kauffman, Daniel Fuerstenau. “What Fuels Growth? A Comparative Analysis of the Scaling Intensity of AI Start-ups”. Under Review. [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, University of British Columbia 2022.

AI technologies automate ever more complex tasks and promise new efficiencies for firms to provide new market offerings and grow. Economists argue that complementarities from AI innovations have not diffused widely enough to yield higher productivity yet though. 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 platform and service start-ups. We use propensity score matching (PSM) to explain the scaling of start-ups and find evidence for sublinear scaling intensity for revenue as a function of labor. Surprisingly, 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 increases in revenue for platform start-ups, while increases were modest for service and AI start-ups. We also consider AI-enabled service start-ups that incorporate both service and AI-based business models and AI-enabled platform start-ups that combine AI and platform business models. AI-enabled service start-ups have a scaling intensity between service and AI start-ups, so they may not yet have achieved scaling benefits because AI adoption requires manual work from human experts. AI-enabled platform start-ups, in contrast, have a higher scaling intensity. Our study provides new perspectives on the role of AI as an emerging technology resource that supports economies of scale and scope for start-ups. 

Strategic Competitive Positioning: An Unsupervised Structural Hole-based Firm-specific Measure

Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Strategic Competitive Positioning: An Unstructured Structural Hole-based Firm-specific Measure”, Under Review. [Submitted: May 13, 2022]

  • doc2vec model of 10-K reports: Link
  • Presented at UBC MIS Seminar 2018, CIST 2019 (Seattle, WA), KrAIS 2019 (Munich, Germany), DS 2021 (online), KrAIS 2021 (Austin, TX), UT Dallas 2022, KAIST 2022, Korea Univ 2022, INFORMS 2022 (Indianapolis, IN)
  • Funded by Sauder Exploratory Grant 2019
  • Research assistants: Raymond Situ, Sahil Jain

In this research methods paper, we propose a firm-specific strategic competitive positioning (SCP) measure to capture a firm’s unique competitive and strategic positioning based on annual corporate filings. Using an unsupervised machine learning approach, we use structural holes, a concept in network theory, to develop and operationalize an SCP measure derived from a strategic similarity matrix of all existing U.S. publicly traded firms. This enables us to construct a robust firm-level SCP measure with minimal human intervention. Our measure dynamically captures competitive positioning across different firms and years without using artificially bounded industry classification systems. We illustrate how the measure dynamically captures firm-level, industry-level, and cross-industry strategic changes. Then, we demonstrate the effectiveness of our measure with an empirical demonstration showing the imprinting effect of SCP at the time of initial public offering (IPO) on the subsequent performance of the firm. The results show that our unsupervised SCP measure predicts post-IPO performance. This paper makes a significant methodological contribution to the information systems and strategic management literature by proposing a network theory-based unsupervised approach to dynamically measure firm-level strategic competitive positioning. The measure can be easily applied to firm-specific, industry-level, and cross-industry research questions across a wide variety of fields and contexts.

Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach (MISQ 2020)

Shin, Donghyuk, Shu He, Gene Moo Lee, Andrew B. Whinston, Suleyman Cetintas, Kuang-Chih Lee (2020) “Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach,” MIS Quarterly, 44(4), pp. 1459-1492. [SSRN]

  • Based on an industry collaboration with Yahoo! Research
  • The first MISQ methods article based on machine learning
  • Presented in WeB (Fort Worth, TX 2015), WITS (Dallas, TX 2015), UT Arlington (2016), Texas FreshAIR (San Antonio, TX 2016), SKKU (2016), Korea Univ. (2016), Hanyang (2016), Kyung Hee (2016), Chung-Ang (2016), Yonsei (2016), Seoul National Univ. (2016), Kyungpook National Univ. (2016), UKC (Dallas, TX 2016), UBC (2016), INFORMS CIST (Nashville, TN 2016), DSI (Austin, TX 2016), Univ. of North Texas (2017), Arizona State (2018), Simon Fraser (2019), Saarland (2021), Kyung Hee (2021), Tennessee Chattanooga (2021), Rochester (2021), KAIST (2021), Yonsei (2021), UBC (2022)

This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model’s power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research.