Author Archives: gene lee

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

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.

 

Go beyond the Local Search: Understanding the Impact of AI Capabilities on Exploratory Innovation

Cui, Victor*, Gene Moo Lee*, Myunghwan Lee*. “Go beyond the Local Search: Understanding the Impact of AI Capabilities on Exploratory Innovation”, Under Review. [Submitted: Jan 10, 2023] (* equal contribution)

  • Research assistant: Raymond Situ

Firms typically depend on technological assets or inter-firm relationships to pursue exploratory innovation. In this paper, we regard Artificial Intelligence (AI) as an exploratory innovation-seeking instrument by which AI may search unexplored resources and thereby broaden the boundary of a firm. Drawing on the theory of bounded rationality and organizational learning, we hypothesize the impact of a firm’s AI capabilities on exploratory innovation and how AI influences traditional boundary-expanding activities. Our empirical investigations, using a novel AI capabilities measure constructed with AI conference and patent datasets, show that AI capabilities have positive impacts on exploratory innovation. In addition, the results show that extant technological assets (i.e., traditional data management capabilities) and ongoing inter-firm relationships (i.e., inter-firm technology collaboration) remedy the constraints on a firm’s innovation-seeking behaviors and that these boundary-expanding activities negatively moderate the positive impact of AI capabilities on exploratory innovation. Our key takeaway is that we investigate how AI affects exploratory innovation using our newly developed AI capability measure, contributing to the body of knowledge on exploratory innovation literature.

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 TikTok, Under Review.

  • Presentations: INFORMS DS (2022), UBC (2022), WITS (2022), ISMS MKSC (2023)
  • API sponsored by Influencer Hunters

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.

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.

 

Reflections on conference organizations in 2021

In 2021, I had great opportunities to serve as an organizer for three events: Program Co-Chair for INFORMS Workshop on Data Science 2021, Workshop Co-Chair for KrAIS Research Workshop 2021, and Minitrack Co-Chair for HICSS 2022 TAEM Minitrack. This post is to reflect my experiences in organizing these events. In sum, I am grateful that I had the opportunity to contribute to my academic communities!

1. INFORMS Workshop on Data Science 2021 (Virtual via Zoom) [DS 2021 Program]

This INFORMS workshop is for data science-oriented IS research. Many of the papers are technical in nature, using various computational and machine learning approaches, to solve a variety of business and societal challenges. The previous workshops were collocated with CIST in the INFORMS Annual Meeting locations. Due to the pandemic, the 2021 workshop was held virtually. There are both positive and negative sides to being virtual. Just focussing on the positive side, because there is no travel cost, many participants from all around the world could participate in the event, although there could be some time zone issues. Thankfully, we could invite many prestigious editors to our panel discussion (many thanks to the editors Andrew Burton-Jones, Alok Gupta, Subodha Kumar, Olivia Sheng, D. J. Wu as well as the moderator Ahmed Abbasi). We also had the great honor to have Jon Kleinberg as the keynote speaker. Last but not least, we had great presentations about many cutting-edge papers on recommender systems, algorithm design, deep learning, personalization, pricing, network analytics, and healthcare. Thanks to all the conference co-chairs (Gautam Pant, Wenjun Zhou, Shawn Mankad), program co-chairs (Yong Ge, Jingjing Zhang), and other organizing committee members. It was great teamwork!

2. KrAIS Research Workshop 2021 (Hybrid in Austin, TX & Zoom) [KrAIS 2021 Program]

This post-ICIS workshop is to promote the scholarship and provide networking opportunities for the AIS members with Korean heritage. ICIS 2021 was held in Austin, TX, and I was looking forward to visiting my second home through this opportunity. We managed to secure a great conference venue (OASIS on Lake Travis). However, due to the COVID-19 variant omicron, many international participants (including myself!) had to cancel their travel plans at the very last moment, hence the organizers had to manage many last-minute changes. Managing a hybrid conference brought interesting challenges: the audio-video delivery between the venue and Zoom, the transition between on-site and online, and registration processes. We had a great panel discussion on the issue of EDI (many thanks to panelists Victoria Yoon, Byungjoon Yoo, Min-Seok Pang, and the moderator Dokyun Lee). Also, I appreciate the support from the KrAIS Co-Presidents (Habin Lee, Byungjoon Yoo) and KrAIS Committee members (Wooje Cho, Kyung Young Lee, Youngsok Bang). Many thanks to my fellow workshop co-chairs (Hyeyoung Hah, JaeHong Park)!

3. HICSS 2022 Technology and Analytics in Emerging Markets (TAEM) Mini-track (Virtual via Zoom) [HICSS 2022 TAEM Mini-track]

Starting from HICSS 2021, Sang-Pil Han, Sungho Park, Wonseok Oh, and I are organizing a mini-track at the HICSS conference. The objective of this mini-track is to nurture a vibrant community between academics and industry on the topic of technology and analytics in emerging markets. Of course, in beautiful Hawaii islands. Unfortunately, we had to do virtual conferences for two consecutive years (we are missing Hawaii!). Fortunately, we had many great paper submissions this year (thanks to the authors who submitted their great work). We had a Zoom session to discuss the accepted papers. We all agreed to meet in person again in Hawaii next year!

4. Summary

When I was a participant in conferences, I didn’t realize all the complexities behind the scene. Now I started to appreciate the significant amount of time and effort put by conference organizers to make such events a reality. Thanks to all the organizers of the numerous conferences and workshops that I attended in my academic life! In 2022, I will be serving as a track co-chair (with Ali Shuyaev and Jing Wang) for ICIS 2022 Data Analytics for Business and Societal Challenges, a track co-chair (with Seung Hyun Kim and Dan J. Kim) for PACIS 2022 Cybersecurity, Privacy, and Ethical Issues, and a conference co-chair (with Jingjing Zhang and Yong Ge) for INFORMS Workshop on Data Science 2022. The reward of good work is more work, but I am happy to keep contributing to our academic communities 🙂

Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.com

Park, Jaecheol, Arslan Aziz, Gene Moo Lee. “Do Incentivized Reviews Poison the Well? Evidence from a Natural Experiment at Amazon.comWorking Paper.

  • Presentations: UBC (2021), KrAIS (2021), WISE (2021), PACIS (2022), SCECR (2022), BU Platform (2022)

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.

Learning Faces to Predict Matching Probability in an Online Dating Market (ICIS 2022)

Kwon, Soonjae, Sung-Hyuk Park, Gene Moo Lee, Dongwon Lee. “Learning Faces to Predict Matching Probability in an Online Dating Market”. In Proceedings of International Conference on Information Systems 2022.

  • 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.

My thoughts on AI, Big Data, and IS Research

Last update: June 10th, 2021

Recently, I had a chance to share my thoughts on how Big Data Analytics and AI will impact Information Systems (IS) research. Thanks to ever-growing datasets (public and proprietary) and powerful computational resources (cloud API, open-source projects), AI and Big Data will be important in IS research in the foreseeable future. If you are an aspiring IS researcher, I believe that you should be able to embrace this and take advantage of this.

First, AI and Big Data are powerful “tools” for IS research. It could be intimidating to see all the fancy new AI techniques. But they are just tools to analyze your data. You don’t need to reinvent the wheel to use them. There are many open-source projects in Python and R that you can use to analyze your data. Also, many cloud services (e.g., Amazon Rekognition, Google Cloud ML, Microsoft Azure ML) allow you to use pre-trained AI models at a modest cost (that your professors can afford). What you need is some working knowledge in programming languages like Python and R. And a high-level understanding of the idea behind algorithms.

Don’t shy away from hands-on programming. Using AI and Big Data tools may not be a competitive advantage in the long run because of the democratization of AI tools. However, I believe it will be the new baseline. So you need to have it in your research toolbox. Specifically, I believe that IS researchers should have a working knowledge of Python/R programming and Linux environment. I recommend these online courses: Data ScienceMachine LearningLinuxSQL, and NoSQL.

Second, AI and Big Data Analytics are creating a lot of interesting new “phenomenon” in personal lives, firms, and societies. How AI and robots will be adopted in the workplace and how that will affect the labor market? Are we losing our jobs? Or can we improve our productivity with AI tools? How AI will be used in professional services by the experts? What are the unintended consequences (such as biases, security, privacy, misinformation) of AI adoptions in the organization and society? And how can we mitigate such issues? There are so many new and interesting research questions.

In order to conduct relevant research, I think that IS researchers should closely follow the emerging technologies. Again, it could be hard to keep up with all the advances. I try to keep up to date by reading industry reports (from McKinsey and Deloitte) and listening to many podcasts (e.g., Freakonomics Radio, a16 Podcasts by Andreessen Horowitz, Lex Fridman Podcast, Stanford’s Entrepreneurial Thought Leaders, HBR’s Exponential View by Azeem Azhar).

I hope this post may help new IS researchers shape their research strategies. I will try to keep updating this post. Cheers!