Tag Archives: AI

Papers on AI, Automation, and Robotics

Last update: May 17, 2024

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

  1. Babina, Tania, Anastassia Fedyk, Alex He, James Hodson (2024) Artificial intelligence, firm growth, and product innovation, Journal of Financial Economics 151.
  2. Eloundou T, Manning S, Mishkin P, Rock D. (2023) GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130.
  3. Acemoglu, Daron and Pascual Restrepo (2022) Tasks, Automation, and the Rise in U.S. Wage Inequality, Econometrica, 90(5): 1973-2016,
  4. 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.
  5. Koch, Michael, Manuylov Ilya, Marcel Smolka (2021) Robots and Firms, The Economic Journal 131(638):2553-2584.
  6. 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.
  7. 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.
  8. Berente, Nicholas, Gu, Bin, Recker, Jan, Santhanam, Radhika. (2021) Special Issue Editor’s Comments: Managing Artificial Intelligence. MIS Quarterly (45: 3) pp. 1433-1450.
  9. Dixon, Jay, Bryan Hong, Lynn Wu (2021) The Robot Revolution: Managerial and Employment Consequences for Firms. Management Science 67(9):5586-5605.
  10. Schanke, Scott, Gordon Burtch, Gautam Ray (2021) Estimating the Impact of “Humanizing” Customer Service Chatbots. Information Systems Research 32(3):736-751.
  11. 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
  12. Graetz, G., Michaels, G. 2018. Robots at work. Review of Economics and Statistics (100:5), pp. 753-768.
  13. 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.

 

Seeing the Unseen: The Effects of Implicit Representation in an Online Dating Platform

Kwon, Soonjae, Gene Moo Lee, Dongwon Lee, Sung-Hyuk Park (2024) “Seeing the Unseen: The Effects of Implicit Representation in an Online Dating Platform,” Working Paper.

  • Previous title: Learning Faces to Predict Matching Probability in an Online Dating Market
  • Presentations: DS (2021), AIMLBA (2021), WITS (2021), ICIS (2022)
  • Preliminary version in ICIS 2022 Proceedings
  • Based on an industry collaboration

This study investigates the effects of implicit preference-based representation on user engagement and matching outcomes in two-sided platforms, focusing on an online dating context. We develop a novel approach using explainable AI and generative AI to create personalized representations that reflect users’ implicit preferences. Through extensive matching simulations, we demonstrate that implicit representation significantly enhances both user engagement and matching outcomes across various recommendation algorithms. Our findings reveal heterogeneous effects driven by positive cross-side and same-side network effects, which vary depending on the gender distribution within the platform. This research contributes to understanding implicit representation in two-sided platforms and offers insights into the transformative potential of generative AI in digital ecosystems.

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 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.

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

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.

Strategic Competitive Positioning: A Structural Hole-based Firm-level Opportunity Construct for Information Systems Research

Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Strategic Competitive Positioning: A Structural Hole-based Firm-level Opportunity Construct for Information Systems Research”, [Latest version: Nov 27, 2024]

  • Previous title: Strategic Competitive Positioning: Unsupervised Operationalization of a Structural Hole-based Firm-specific Construct
  • 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

We build on Burt’s structural hole concept to theorize a firm-specific strategic competitive positioning (SCP) construct for information systems (IS) research. Using unsupervised document embeddings, we operationalize the SCP construct to capture a firm’s relative competitive and strategic positioning in a similarity matrix of U.S. public firms based on their annual reports. Our construct dynamically captures competitive positioning across firms and years, relying on neither artificially bounded industry classification systems nor significant expert intervention to construct the measure, ensuring a more efficient and adaptable approach. We demonstrate the effectiveness of this construct through a series of empirical analyses investigating the effects of SCP on firm value and survival. The results show that our measure outperforms existing measures in successfully predicting post-IPO performance. This paper makes significant contributions to the IS literature by proposing an organizational theory-based unsupervised approach to dynamically conceptualize and measure firm-level strategic competitive positioning from unstructured corporate disclosure documents.

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), Temple (2023)

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.

Developing Cyber Risk Assessment Framework for Cyber Insurance: A Big Data Approach (KIRI Research Report 2018)

Lee, G. M. (2018). Developing Cyber Risk Assessment Framework for Cyber Insurance: A Big Data Approach (in Korean). KIRI Research Report 2018-15.

As our society is heavily dependent on information and communication technology, the associated risk has also significantly increased. Cyber insurance has been emerged as a possible means to better manage such cyber risk. However, the cyber insurance market is still in a premature stage due to the lack of data sharing and standards on cyber risk and cyber insurance. To address this issue, this research proposes a data-driven framework to assess cyber risk using externally observable cyber attack data sources such as outbound spam and phishing websites. We show that the feasibility of such an approach by building cyber risk assessment reports for Korean organizations. Then, by conducting a large-scale randomized field experiment, we measure the causal effect of cyber risk disclosure on organizational security levels. Finally, we develop machine-learning models to predict data breach incidents, as a case of cyber incidents, using the developed cyber risk assessment data. We believe that the proposed data-driven methods can be a stepping-stone to enable information transparency in the cyber insurance market.

Predicting Litigation Risk via Machine Learning

Lee, Gene Moo*, James Naughton*, Xin Zheng*, Dexin Zhou* (2020) “Predicting Litigation Risk via Machine Learning,” Working Paper. [SSRN] (* equal contribution)

This study examines whether and how machine learning techniques can improve the prediction of litigation risk relative to the traditional logistic regression model. Existing litigation literature has no consensus on a predictive model. Additionally, the evaluation of litigation model performance is ad hoc. We use five popular machine learning techniques to predict litigation risk and benchmark their performance against the logistic regression model in Kim and Skinner (2012). Our results show that machine learning techniques can significantly improve the predictability of litigation risk. We identify two best-performing methods (random forest and convolutional neural networks) and rank the importance of predictors. Additionally, we show that models using economically-motivated ratio variables perform better than models using raw variables. Overall, our results suggest that the joint consideration of economically-meaningful predictors and machine learning techniques maximize the improvement of predictive litigation models.