Tag Archives: analytics

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.

 

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. https://sites.google.com/view/myunghwanlee/home

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.

 

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

Zhang, Xiaoke, Mi Zhou, Gene Moo Lee “How Does AI-Generated Voice Affect Online Video Creation? Evidence from TikTok,” Working Paper.

  • Presentations: INFORMS DS (2022), UBC (2022), WITS (2022), Yonsei (2023), POSTECH (2023), ISMS MKSC (2023), CSWIM (2023), KrAIS Summer (2023), Dalhousie (2023), CIST (2023), Temple (2024), Santa Clara U (2024), Wisconsin Milwaukee (2024)
  • Best Student Paper Nomination at CIST 2023; Best Paper Runner-Up Award at KrAIS 2023
  • Media coverage: [UBC News] [Global News]
  • API sponsored by Ensemble Data
  • SSRN version: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4676705

The rising demand for online video content has fostered one of the fastest-growing markets as evidenced by the growing popularity of platforms like TikTok. In response to the challenges of video creation, these platforms are increasingly incorporating artificial intelligence (AI) to support creators in their video creation process. However, little is known about how AI integration influences online content creation. Our paper aims to address this gap by investigating the impact of AI-generated voice on video creators’ productivity and creative patterns. Using a comprehensive 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. Moreover, we find that the use of AI voice enhances video novelty across image, audio, and text modalities, suggesting its role in reducing workload on routine tasks and fostering creative exploration. Lastly, our study also uncovers a disinhibition effect, where creators tend to 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 AI reshapes online video creation, providing important implications for creators, platforms, and policymakers in the creator 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.

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 🙂

My thoughts on AI, Big Data, and IS Research

Last update: May 31, 2024

Back in 2021, 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: AI Fundamentals, Data Science, Machine Learning, Linux, SQL, and NoSQL.

Second, AI and Big Data Analytics are creating a lot of interesting new “phenomena” in personal lives, firms, and societies. How AI and robots will be adopted in the workplace and how will that affect the labor market? Are we losing our jobs? Or can we improve our productivity with AI tools? How will experts use AI in professional services? What are the unintended consequences (such as biases, security, privacy, and 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.

To stay relevant, I think that IS researchers should closely follow 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).

For UBC current and prospective students, here are some resources:

For educators, I have shared my teaching experience using AI in May 2024. You can find the slide deck here.

I hope this post may help people shape their research, teaching, and career strategies. I will try to keep updating this post. Cheers!

 

 

IS / Marketing Papers on Multimodal Data Analytics (Image, Video, Audio)

Last update: Sep 7, 2023

With the advent of social media and mobile platforms, visual and multimodal data are becoming the first citizen in big data analytics research. Compared to textual data that require significant cognitive efforts to comprehend, visual data (such as images and videos) can easily convey the message from the content creator to the general audience. To conduct large-scale studies on such data types, researchers need to use machine learning and computer vision approaches. In this post, I am trying to organize studies in Information Systems, Marketing, and other management disciplines that leverage large-scale analysis of image and video datasets. The papers are ordered randomly:

  1. Yang, Yi, Yu Qin, Yangyang Fan, Zhongju Zhang (2023). Unlocking the Power of Voice for Financial Risk Prediction: A Theory-Driven Deep Learning Design Approach. MIS Quarterly 47(1): 63-96.
  2. Ceylan, G., Diehl, K., & Proserpio, D. (2023). EXPRESS: Words Meet Photos: When and Why Visual Content Increases Review Helpfulness. Journal of Marketing Research, forthcoming.
  3. Alex Burnap, John R. Hauser, Artem Timoshenko (2023) Product Aesthetic Design: A Machine Learning Augmentation. Marketing Science, forthcoming.
  4. Gao, Jia, Ying Rong, Xin Tian, Yuliang Yao (2023) Improving Convenience or Saving Face? An Empirical Analysis of the Use of Facial Recognition Payment Technology in Retail. Information Systems Research, forthcoming.
  5. Guan, Yue, Yong Tan, Qiang Wei, Guoqing Chen (2023) When Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics. Information Systems Research, Forthcoming.
  6. Son, Y., Oh, W., Im, I. (2022) The Voice of Commerce: How Smart Speakers Reshape Digital Content Consumption and Preference. MIS Quarterly, forthcoming.
  7. Hou, J., Zhang, J., & Zhang, K. (2022). Pictures that are Worth a Thousand Donations: How Emotions in Project Images Drive the Success of Crowdfunding Campaigns? An Image Design Perspective. MIS Quarterly, Forthcoming.
  8. Lysyakov, Mikhail, Siva Viswanathan (2022) Threatened by AI: Analyzing Users’ Responses to the Introduction of AI in a Crowd-Sourcing Platform. Information Systems Research, Forthcoming.
  9. Hanwei Li, David Simchi-Levi, Michelle Xiao Wu, Weiming Zhu (2022) Estimating and Exploiting the Impact of Photo Layout: A Structural Approach. Management Science, Forthcoming.
  10. Bharadwaj, N., Ballings, M., Naik, P. A., Moore, M, Arat, M. M. (2022) “A New Livestream Retail Analytics Framework to Assess the Sales Impact of Emotional Displays,” Journal of Marketing, 86(1): 24-47.
  11. Chen, Z., Liu, Y.-J., Meng, J., Wang, Z. (2022) “What’s in a Face? An Experiment on Facial Information and Loan-Approval Decision“, Management Science, forthcoming.
  12. Lu, T., Wang, A., Yuan, X., Zhang, X. (2020) “Visual Distortion Bias in Consumer Choices,” Management Science, forthcoming.
  13. Zhou, M., Chen, G. H., Ferreira, P., Smith, M. D. (2021) “Consumer Behavior in the Online Classroom: Using Video Analytics and Machine Learning to Understand the Consumption of Video Courseware,” Journal of Marketing Research 58(6): 1079-1100.
  14. Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, Kannan Srinivasan (2021) What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features. Management Science 68(8):5644-5666.
  15. Gunarathne, P., Rui, H., Seidmann, A. (2021) “Racial Bias in Customer Service: Evidence from Twitter,” Information Systems Research 33(1): 43-54.
  16. Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., Lee, K.-C. (2020) “Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach,” MIS Quarterly 44(4): 1459-1492. [Details]
  17. Li, Y., Xie, Y. (2020) “Is a Picture Worth a Thousand Words? An Empirical Study of Image Content and Social Media Engagement,” Journal of Marketing Research 57(2): 1-19.
  18. Zhang, Q., Wang, W., Chen, Y. (2020) “Frontiers: In-Consumption Social Listening with Moment-to-Moment Unstructured Data: The Case of Movie Appreciation and Live comments,” Marketing Science 39(2).
  19. Liu, L., Dzyabura, D., Mizik, N. (2020) “Visual Listening In: Extracting Brand Image Portrayed on Social Media,” Marketing Science 39(4): 669-686.
  20. Peng, L., Cui, G., Chung, Y., Zheng, W. (2020) “The Faces of Success: Beauty and Ugliness Premiums in E-Commerce Platforms,” Journal of Marketing 84(4): 67-85.
  21. Liu, X., Zhang, B., Susarla, A., Padman, R. (2020) “Go to YouTube and Call Me in the Morning: Use of Social Media for Chronic Conditions,” MIS Quarterly 44(1b): 257-283.
  22. Zhao, K., Hu, Y., Hong, Y., Westland, J. C. (2020) “Understanding Characteristics of Popular Streamers in Live Streaming Platforms: Evidence from Twitch.tv,” Journal of the Association for Information Systems, Forthcoming.
  23. Ordenes, F. V., Zhang, S. (2019) “From words to pixels: Text and image mining methods for service research,” Journal of Service Management 30(5): 593-620.
  24. Wang, Q., Li, B., Singh, P. V. (2018) “Copycats vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis,” Information Systems Research 29(2): 273-291.
  25. Lu, S., Xiao, L., Ding, M. (2016) “A Video-Based Automated Recommender (VAR) System for Garments,” Marketing Science 35(3): 484-510.
  26. Xiao, L., Ding, M. (2014) “Just the Faces: Exploring the Effects of Facial Features in Print Advertising,” Marketing Science 33(3), 315-461.
  27. Suh, K.-S., Kim, H., Suh, E. K. (2011) “What If Your Avatar Looks Like You? Dual-Congruity Perspectives for Avatar Use,” MIS Quarterly 35(3), 711-729.
  28. Todorov, A., Porter, J. M. (2014) “Misleading First Impressions: Different for Different Facial Images of the Same Person“, Psychological Science 25(7): 1404-1417.
  29. Todorov, A., Madnisodza, A. N., Goren, A., Hall, C. C. (2005) “Inferences of Competence from Faces Predict Election Outcomes“, Science 308(5728): 1623-1626.
  30. Mueller. U., Mazur, A. (1996) “Facial Dominance of West Point Cadets as a Predictor of Later Military Rank“, Social Forces 74(3): 823-850.
  31. Lee, H, Nam, K. “When Machine Vision Meets Human Fashion: Effects of Human Intervention on the Efficiency of CNN-Driven Recommender Systems in Online Fashion Retail”, Working Paper.
  32. Lysyhakov M, Viswanathan S (2021) “Threatened by AI: Analyzing users’ responses to the introduction of AI in a crowd-sourcing,” Working Paper.
  33. Park, S., Lee, G. M., Shin, D., Han, S.-P. (2020) “Targeting Pre-Roll Ads using Video Analytics,” Working Paper.
  34. Choi, A., Ramaprasad, J., So, H. (2021) Does Authenticity of Influencers Matter? Examining the Impact on Purchase Decisions, Working Paper.
  35. Park, J., Kim, J., Cho, D., Lee, B. Pitching in Character: The Role of Video Pitch’s Personality Style in Online Crowdsourcing, Working Paper.
  36. Yang, J., Zhang, J., Zhang Y. (2021) First Law of Motion: Influencer Video Advertising on TikTok, Working Paper.
  37. Davila, A., Guasch (2021) Manager’s Body Expansiveness, Investor Perceptions, and Firm Forecast Errors and Valuation, Working Paper.
  38. Peng, L., Teoh, S. H., Wang, U., Yan, J. (2021) Face Value: Trait Inference, Performance Characteristics, and Market Outcomes for Financial Analysts, Working Paper.
  39. Zhang, S., Friedman, E., Zhang, X., Srinivasan, K., Dhar, R. (2020) Serving with a Smile on Airbnb: Analyzing the Economic Returns and Behavioral Underpinnings of the Host’s Smile,” Working Paper.
  40. Park, K., Lee, S., Tan, Y. (2020) “What Makes Online Review Videos Helpful? Evidence from Product Review Videos on YouTube,” UW Working Paper.
  41. Doosti, S., Lee, S., Tan, Y. (2020) “Social Media Sponsorship: Metrics for Finding the Right Content Creator-Sponsor Matches,” UW Working Paper.
  42. Koh, B., Cui, F. (2020) “Give a Gist: The Impact of Thumbnails on the View-Through of Videos,” KU Working Paper.
  43. Hou J.R., Zhang J., Zhang K. (2018) Can title images predict the emotions and the performance of crowdfunding projects? Workshop on e-Business.

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.