Author Archives: gene lee

Anatomy of Phishing Tactics and Susceptibility

Bera, Debalina, Gene Moo Lee, Dan J. Kim “Anatomy of Phishing Tactics and Susceptibility: An Investigation of the Dynamics of Phishing Tactics and Contextual Traits in Susceptibility,” Working Paper.

Phishing is a deceptive tactic to create a front of apparent credibility to fraudulently acquire sensitive personal or financial information from an unsuspecting user or espionage system by infiltrating malware or crimeware. Despite automated technological solutions and training interventions, recent phishing statistics show that specifically few phishing tactics are increasing users’ phishing susceptibility (PS). Further, assessing the moderating role of phishing contextual traits in the relationship between phishing tactics and PS indicates the importance of their trait differences. Based on theoretical postulation, employing a sequential mixed method design, and using two sets of data (simulated phishing penetration testing results and scenario-based experiments), we examine the effect of phishing tactics along with the moderating role of individual phishing contextual traits on PS. This study extends the theoretical boundary relevant to phishing tactics and provides practical guidance to identify the most dangerous phishing tactics that increase PS and phishing contextual traits that help to combat phishing attacks.

From Spacing to Scheduling: The Effect of Seller Posting Time Strategies for Short Video Advertising

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.

Xiaoke Zhang’s Master’s Thesis

Xiaoke Zhang (2023). “How Does AI-Generated Voice Affect Online Video Creation? Evidence from TikTok”, Master’s Thesis, University of British Columbia

Supervisors: Gene Moo Lee, Mi Zhou

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 in 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.


The Effect of Mobile Device Management on Work-from-home Productivity: Insights from U.S. Public Firms

Park, Jaecheol, Myunghwan Lee, Gene Moo Lee “The Effect of Mobile Device Management on Work-from-home Productivity: Insights from U.S. Public Firms”, Work-in-Progress.

  • Based on an industry collaboration
  • Presentations: UBC 2023, MSISR 2023, KrAIS 2023, WeB 2023, AOM 2024
  • Best Paper Nomination at WeB 2023

The use of mobile IT, providing employees with accessibility, flexibility, and connectivity, has become increasingly vital for businesses, especially for work-from-home during the COVID-19 pandemic. However, despite its prevalence and importance in the industry, the business value of mobile device management (MDM) and its role in establishing digital resilience remain underexplored in the literature. To address this research gap, our study examines the effect of MDM on a firm’s resilience to the pandemic. Drawing on the resource-based view (RBV), we utilize novel proprietary data from a global MDM solution provider for U.S. public firms. We find that firms with MDM have better financial performance during the pandemic, demonstrating greater resilience to the shock. Additionally, we explore the moderating role of external and internal factors, revealing that firms with high environmental munificence or those with low IT capabilities experience greater resilience effects from MDM. Furthermore, we observe heterogeneous effects across industries that firms in industry sectors demanding greater mobility have a greater resilience effect from MDM. This study contributes to the information systems literature by emphasizing the business value of MDM and its crucial role in building digital resilience.

Myunghwan Lee’s PhD Proposal: Three Essays on AI Strategies and Innovation

Myunghwan Lee (2023) “Three Essays on AI Strategies and Innovation”, Ph.D. Dissertation Proposal, University of British Columbia.

Supervisor: Gene Moo Lee

Artificial Intelligence (AI) technologies, along with the explosive growth of digitized data, are transforming many industries and our society. While both academia and industry consider AI closely intertwined with innovation, we still have limited knowledge of the business and economic values of AI on innovation. This three-essay dissertation seeks to address this gap (i) by proposing a novel firm-level measure to identify strategically innovative firms; (ii) by examining how firm-level AI capabilities affect knowledge innovation; and (iii) by investigating the impact of robotics, embodied AI with a physical presence, on operational innovation.

In the first essay, we propose a novel firm-level measure, Strategic Competitive Positioning (SCP), to identify distinctive strategic positioning (i.e., first-movers, second-movers) and competition relationships. Drawing on network theory, we develop a structural hole-based, dynamic, and firm-specific SCP measure. Notably, this SCP measure is constructed using unsupervised machine-learning and network analytics approaches with minimal human intervention. Using a large dataset of 10-K annual reports from 13,476 public firms in the U.S., we demonstrate the value of the proposed measure by examining the impact of SCP on subsequent IPO performance.

In the second essay, we study the impact of firm-level AI capabilities on exploratory innovation to determine how AI’s value-creation process can facilitate knowledge innovation. Drawing on March and Simon (1958), we theorize how AI capabilities can help firms overcome bounded rationality and pursue exploratory innovation. We compiled a unique dataset consisting of 54,649 AI conference publications, 3 million patent filings, and 1.9 million inter-firm transactions to test the hypotheses. The findings show that a firm’s AI capabilities have a positive impact on exploratory innovation, and interestingly that conventional exploratory innovation-seeking approaches (e.g., traditional data management capabilities and inter-firm technology collaborations) negatively moderate the positive impact of AI capabilities on exploratory innovation.

The impact of AI technologies can be beyond knowledge innovation. Embodied AI technologies, specifically robotics, are driving operational innovation in manufacturing and service industries. While industrial robots designed for pre-defined tasks in controlled environments are extensively studied, little is known about the impact of AI-based service robots designed for customer-facing dynamic environments. In the third essay, we seek to examine how service robots can affect operational efficiency and service quality using the case of the hospitality industry. The preliminary results from a difference-in-differences model using a dataset of 4,610 restaurants in Singapore demonstrate that service robot adoption increases customer satisfaction, specifically through perceived service quality. To validate the initial result and further explore underlying mechanisms, we plan to collect additional datasets from different geographic areas and industries.


Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation

Lee, Myunghwan, Victor Cui, Gene Moo Lee. “Disrupt with AI: The Impact of Deep Learning Capabilities on Exploratory Innovation”, AOM 2023

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.

Generative AI and Creator Economy: Investigating the Effects of AI-Generated Voice on Online Video Creation

Zhang, Xiaoke, Mi Zhou, Gene Moo Lee Generative AI and Creator Economy: Investigating the Effects of AI-Generated Voice on Online Video Creation, R&R at Management Science.

Generative artificial intelligence (AI) has the potential to revolutionize the creative industry by reshaping the human creative process. We explore the potential of generative AI in the creator economy by investigating the effects of AI-generated voice adoption on creators’ productivity and creative patterns on TikTok, one of the world’s largest video-sharing platforms. Using a unique dataset of 554,252 videos from 4,691 TikTok creators, we conduct multimodal analyses of the video data to detect the adoption of AI voice and to quantify video characteristics. We then estimate the adoption effects using a stacked difference-in-differences model coupled with propensity score matching. Our results suggest that AI voice adoption significantly increases creator productivity. This effect is larger among less experienced or less popular creators, suggesting an equalizing effect of generative AI. Moreover, we find that the use of AI voice enhances video novelty across image, audio, and text modalities, especially among experienced creators, suggesting its role in reducing routine workload and fostering creative exploration. Lastly, our study also uncovers a disinhibition effect, where creators conceal their identities with the AI voice and exert more negative sentiments because of diminished social image concerns. Our paper provides the first empirical evidence of how generative AI reshapes online video creation.

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.

IS papers on Cybersecurity

Last update: Jan 18, 2022

In this post, I gathered recent IS publications (2010-current) on the topic of cybersecurity. It is by no means an exhaustive list of the topic. This does not cover other related topics such as privacy and ethics.

  1. Jacob Haislip, Jee-Hae Lim, Robert Pinsker (2021) The Impact of Executives’ IT Expertise on Reported Data Security Breaches. Information Systems Research 32(2):318-334.
  2. Ahmed Abbasi, David Dobolyi, Anthony Vance, Fatemeh Mariam Zahedi (2021) The Phishing Funnel Model: A Design Artifact to Predict User Susceptibility to Phishing Websites. Information Systems Research 32(2):410-436.
  3. Yunhui Zhuang, Yunsik Choi, Shu He, Alvin Chung Man Leung, Gene Moo Lee & Andrew Whinston (2020) Understanding Security Vulnerability Awareness, Firm Incentives, and ICT Development in Pan-Asia, Journal of Management Information Systems, 37:3, 668-693.
  4. Qian Tang & Andrew B. Whinston (2020) Do Reputational Sanctions Deter Negligence in Information Security Management? A Field Quasi‐Experiment, Production and Operations Management 29(2):410-427.
  5. Yoo, Chul & Goo, Jahyun & Rao, Raghav. (2020). Is Cybersecurity a Team Sport? A Multilevel Examination of Workgroup Information Security Effectiveness. MIS Quarterly. 44. 907-931.
  6. Mohammadreza Ebrahimi, Jay F. Nunamaker Jr. & Hsinchun Chen (2020) Semi-Supervised Cyber Threat Identification in Dark Net Markets: A Transductive and Deep Learning Approach, Journal of Management Information Systems, 37:3, 694-722
  7. Sebastian W. Schuetz, Paul Benjamin Lowry, Daniel A. Pienta & Jason Bennett Thatcher (2020) The Effectiveness of Abstract Versus Concrete Fear Appeals in Information Security, Journal of Management Information Systems, 37:3, 723-757.
  8. Che-Wei Liu, Peng Huang & Henry C. Lucas Jr. (2020) Centralized IT Decision Making and Cybersecurity Breaches: Evidence from U.S. Higher Education Institutions, Journal of Management Information Systems, 37:3, 758-787.
  9. Ravi Sen, Ajay Verma & Gregory R. Heim (2020) Impact of Cyberattacks by Malicious Hackers on the Competition in Software Markets, Journal of Management Information Systems, 37:1, 191-216
  10. John D’Arcy, Idris Adjerid, Corey M. Angst, Ante Glavas (2020) Too Good to Be True: Firm Social Performance and the Risk of Data Breach. Information Systems Research 31(4):1200-1223.
  11. Zan Zhang, Guofang Nan, Yong Tan (2020) Cloud Services vs. On-Premises Software: Competition Under Security Risk and Product Customization. Information Systems Research 31(3):848-864.
  12. Terrence August, Duy Dao, Kihoon Kim (2019) Market Segmentation and Software Security: Pricing Patching Rights. Management Science 65(10):4575-4597.
  13. Seung Hyun Kim, Juhee Kwon (2019) How Do EHRs and a Meaningful Use Initiative Affect Breaches of Patient Information?. Information Systems Research 30(4):1184-1202.
  14. Kai-Lung Hui, Ping Fan Ke, Yuxi Yao, Wei T. Yue (2019) Bilateral Liability-Based Contracts in Information Security Outsourcing. Information Systems Research 30(2):411-429.
  15. Victor Benjamin, Joseph S. Valacich, and Hsinchun Chen (2019) DICE-E: a framework for conducting darknet identification, collection, evaluation with ethics. MIS Quarterly 43(1):1–22.
  16. Indranil Bose and Alvin Chung Man Leung (2019) Adoption of identity theft countermeasures and its short- and long-term impact on firm value. MIS Quarterly 43(1):313–328.
  17. Corey M. Angst, Emily S. Block, John D’Arcy, and Ken Kelley (2017) When do IT security investments matter? Accounting for the influence of institutional factors in the context of healthcare data breaches. MIS Quarterly 41(3):893–916.
  18. Orcun Temizkan, Sungjune Park, Cem Saydam (2017) Software Diversity for Improved Network Security: Optimal Distribution of Software-Based Shared Vulnerabilities. Information Systems Research 28(4):828-849.
  19. Shu He, Gene Moo Lee, Sukjin Han, Andrew B. Whinston (2016) How Would Information Disclosure Influence Organizations’ Outbound Spam Volume? Evidence from a Field Experiment. Journal of Cybersecurity 2(1), pp. 99-118.
  20. Yonghua Ji, Subodha Kumar, Vijay Mookerjee (2016) When Being Hot Is Not Cool: Monitoring Hot Lists for Information Security. Information Systems Research 27(4):897-918.
  21. Karthik Kannan, Mohammad S. Rahman, Mohit Tawarmalani (2016) Economic and Policy Implications of Restricted Patch Distribution. Management Science 62(11):3161-3182.
  22. Chul Ho Lee, Xianjun Geng, Srinivasan Raghunathan (2016) Mandatory Standards and Organizational Information Security. Information Systems Research 27(1):70-86.
  23. Jingguo Wang, Manish Gupta, and H. Raghav Rao (2015) Insider threats in a financial institution: Analysis of attack-proneness of information systems applications. MIS Quarterly 39(1):91–112.
  24. Jingguo Wang, Nan Xiao, H. Raghav Rao (2015) Research Note—An Exploration of Risk Characteristics of Information Security Threats and Related Public Information Search Behavior. Information Systems Research 26(3):619-633.
  25. Sabyasachi Mitra, Sam Ransbotham (2015) Information Disclosure and the Diffusion of Information Security Attacks. Information Systems Research 26(3):565-584.
  26. Debabrata Dey, Atanu Lahiri, and Guoying Zhang (2014) Quality competition and market segmentation in the security software market. MIS Quarterly 38(2):589–606.
  27. Seung Hyun Kim and Byung Cho Kim (2014) Differential effects of prior experience on the malware resolution process. MIS Quarterly 38(3):655–678.
  28. Ryan T. Wright, Matthew L. Jensen, Jason Bennett Thatcher, Michael Dinger, Kent Marett (2014) Research Note—Influence Techniques in Phishing Attacks: An Examination of Vulnerability and Resistance. Information Systems Research 25(2):385-400.
  29. Asunur Cezar, Huseyin Cavusoglu, Srinivasan Raghunathan (2013) Outsourcing Information Security: Contracting Issues and Security Implications. Management Science 60(3):638-657.
  30. Xia Zhao, Ling Xue & Andrew B. Whinston (2013) Managing Interdependent Information Security Risks: Cyberinsurance, Managed Security Services, and Risk Pooling Arrangements, Journal of Management Information Systems, 30:1, 123-152.
  31. Chul Ho Lee, Xianjun Geng, Srinivasan Raghunathan, (2012) Contracting Information Security in the Presence of Double Moral Hazard. Information Systems Research 24(2):295-311.
  32. Ransbotham, S., Mitra, S., & Ramsey, J. (2012). Are Markets for Vulnerabilities Effective? MIS Quarterly36(1), 43–64.
  33. Gupta, A., & Zhdanov, D. (2012). Growth and Sustainability of Managed Security Services Networks: An Economic Perspective. MIS Quarterly36(4), 1109–1130.
  34. Kai-Lung Hui, Wendy Hui & Wei T. Yue (2012) Information Security Outsourcing with System Interdependency and Mandatory Security Requirement, Journal of Management Information Systems, 29:3, 117-156.
  35. Caliendo, M., Clement, M., Papies, D., & Scheel-Kopeinig, S. (2012). Research Note: The Cost Impact of Spam Filters: Measuring the Effect of Information System Technologies in Organizations. Information Systems Research23(3), 1068–1080.
  36. August, T., & Tunca, T. I. (2011). Who Should Be Responsible for Software Security? A Comparative Analysis of Liability Policies in Network Environments. Management Science57(5), 934–959.
  37. Chen, P., Kataria, G., & Krishnan, R. (2011). Correlated Failures, Diversification, and Information Security Risk Management. MIS Quarterly35(2), 397–422.
  38. Mookerjee, V., Mookerjee, R., Bensoussan, A., & Yue, W. T. (2011). When Hackers Talk: Managing Information Security Under Variable Attack Rates and Knowledge Dissemination. Information Systems Research22(3), 606–623.
  39. Galbreth, M. R., & Shor, M. (2010). The Impact of Malicious Agents on the Enterprise Software Industry. MIS Quarterly34(3), 595–612.
  40. Mahmood, M. A., Siponen, M., Straub, D., Rao, H. R., & Raghu, T. S. (2010). Moving Toward Black Hat Research in Information Systems Security: An Editorial Introduction to the Special Issue. MIS Quarterly34(3), 431–433.

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.