MIS Distinguished Speaker Series

Temple University

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Artificial Intelligence

Mar 17 – Sang-Pil Han – “AI Effectiveness, Task Difficulty, and Employee Income in the Gig Economy: When AI is the Default Service Provider Rather than Humans”

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Time: Friday, 17 March 2023, 10:30–12:00
Room: LW420

Abstract

Increasingly, artificial intelligence (AI) serves as a frontline operator, while humans perform backend operations. Despite growing firms in the gig economy employing a business model with AI as the default service provider, the literature is limited regarding the impact of such a model on employee income. To address such limitations, this research proposes an AI-first service framework, which asserts that AI initially attempts to solve tasks, but upon unsatisfactory service outcome, customers pay a fee for employee assistance with such tasks. To empirically investigate our proposed framework, we partner with an AI-first learning app, where AI and tutors are the default and on-request service providers, respectively. We use tutor-level observational data and find that as AI, the default service provider, becomes more effective, tutor income is mediated by changes in task volume and margin, but that the pathways differ depending on employee expertise. Findings from our granular data show that, as AI effectiveness increases, tasks that are difficult due to their broad coverage across topics are passed on to tutors such that low-expertise (vs. high-expertise) tutors accept fewer tasks. We also conduct a field experiment at the customer level to show that customers are willing to pay for extra assistance from tutors, despite receiving free service from AI, for tasks that are difficult due to in-depth knowledge required within a topic. We discuss the theoretical implications of our findings and practical ramifications to effectively manage and develop human competency in the era of an AI-driven economy.

Bio

Sang-Pil Han is an Associate Professor of Information Systems in the W. P. Carey School of Business at Arizona State University. His research focuses on artificial intelligence, digital platforms, and business analytics. His research has been published in top-tier academic journals such as Management Science, Management Information Systems Quarterly, Information Systems Research and Journal of Marketing, and featured in Harvard Business Review and BBC News. He has received grants from the Marketing Science Institute and Wharton Interactive Media Initiative, the NET Institute, the Wharton Customer Analytics Initiative, the Korea Research Foundation, the Hong Kong General Research Fund, as well as private companies. At ASU, he was a Co-Faculty Director for the Master of Science in Business Analytics program. He served as an Associate Editor at Information Systems Research. He advises a variety of organizations, including tech startups like Mathpresso, a leading AI-powered education platform, and RoundIn, an online golf learning platform, as well as non-profits like Simple Steps, a 501c3 organization that assists female immigrant talent in achieving their professional goals. In his spare time, he enjoys playing golf with his wife and two daughters.

Oct 28 – Lynn Wu – “Innovation Strategy after IPO: How AI Analytics Spurs Innovation after IPO”

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Time: Friday, 28 October 2022, 10:30–12:00
Room: LW420

Lynn Wu
Associate Professor of Operations, Information and Decisions
The Wharton School, The University of Pennsylvania
https://oid.wharton.upenn.edu/profile/wulynn/

Abstract

We examine the role of AI analytics in facilitating innovation in firms that have gone through IPO. Using patent data on over 1,000 publicly traded firms, we find that firms acquiring AI analytics capability post-IPO experience less of a decline in innovation quality compared to similar firms that have not acquired that capability. This effect is greater when only machine learning capabilities are considered. Moreover, we find this sustained rate of innovation is driven principally by the continued development of innovations that combine existing technologies into new ones—a form of innovation that is especially well supported by analytics. By examining three main mechanisms that hampered post-IPO innovation, we find that AI analytics can ameliorate the pressure to meet short-term financial goals and disclosure requirements. However, it has limited effect in addressing managerial incentives. For firms with long product cycles, the disclosure effect is reduced to a greater extent than it is for those with short cycles. Overall, our results show the importance of examining technology as a critical input factor in innovation. We show that the increased deployment of analytics may reduce some of the innovative penalties suffered by IPOs, and that investors and managers can potentially mitigate post-IPO reductions in innovative output by directing capital acquired in the IPO process to the acquisition of AI analytics capabilities.

Bio

Her research examines how emerging information technologies, such as artificial intelligence and analytics, affect innovation, business strategy, and productivity. Specifically, her work follows three streams. In the first stream, she examines how data analytics and artificial intelligence affect firm innovation, business strategy, labor demand, and productivity for both large firms and startups. In her second stream, she studies how enterprise social media and online platforms affect work performance, career trajectories, entrepreneurship success, and the formation of new type of biases that arise from using technologies. In her third stream of research, Lynn leverages fine-grained nanodata available through online digital traces to predict economic indicators such as real estate trends, labor trends and product adoption. Lynn has published articles in economics, management and computer science. Her work has been widely covered by media outlets, including, NPR, the Wall Street Journal, Businessweek, New York Times, Forbes, and The Economist. She has won numerous awards such as Early Career awards from INFORMS and AIS, best paper awards from Information System Research, AIS, ICIS, HICSS, CHITA, and Kauffman. She has also won the Dean’s teaching award.

Sep 24 – Lauren Rhue to present “Man vs. Machine: The Substitutability of AI and Expert Evaluations of Initial Coin Offerings (ICOs)”

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Man vs. Machine: The Substitutability of AI and Expert Evaluations of Initial Coin Offerings (ICOs)

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Lauren Rhue

Assistant Professor of Information Systems
Department of Decision, Operations and Information Technologies
Robert H. Smith School of Business
University of Maryland

Friday, Sep 24

9 – 10 am | Zoom

Abstact:

Initial coin offerings (ICOs) were heralded as a popular method for emerging blockchain and technology ventures to raise capital for their businesses; however, several high-profile ICO scams generated concerns about ICO legitimacy. We examine an ICO-rating platform that provides two evaluation sources, artificial intelligence (AI) and experts, as well as qualitative and quantitative expert evaluations to evaluate ICOs. This study compares the informativeness of the information sources and information types to understand how AI ratings for uncertain quality items, like ICOs, compares to the expert ratings. Using dual-processing theory and cognitive biases, we posit substitutability for quantitative evaluations but complementarity between quantitative and qualitative evaluations. Using nearly 5,000 ICOs and more than 14,000 expert evaluations, we find that 1) experts’ decisions on which ICOs to evaluate contain relevant information, 2) experts and AI quantitative evaluation are substitutes, and 3) quantitative evaluations complement qualitative evaluations. Our paper makes several contributions to the information systems literature related to the substitutability of automation systems for online human reviews, the different processing pathways for qualitative and quantitative evaluations, and the unexpected benefit of cognitive biases.

Oct 30 – Gordon Gao to present “How Artificial Intelligence Affects Human Performance in Medical Chart Coding”

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How Artificial Intelligence Affects Human Performance in Medical Chart Coding

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Guodong (Gordon) Gao

Professor
Director, Inovalon Artificial Intelligence Lab for Advanced Insights
Co-Director, Center for Health Information and Decision Systems
Robert H. Smith School of Business
University of Maryland

Friday, Oct 30

9:00 – 10:00 am | Zoom

Abstact:

While the impact of artificial intelligence (AI) on jobs has generated considerable discussion and debate, little is known about how AI affects knowledge worker productivity. We developed an AI solution for medical chart coding in a publicly traded company and then evaluated its impact on productivity regarding coders’ job experience. We find evidence that AI improves worker productivity overall. However, different from existing studies on skill biased technological change, we find that seniority goes the opposite way: the productivity of senior workers has a much less productivity boost from the use of AI than that of junior workers. To uncover the mechanism behind this surprising finding, we look at the task specific experience. Our results confirm the existence of complementarity between human experience and AI. Further analysis reveals that the performance discrepancy of job experience is attributable to senior user resistance. This paper provides new empirical insights into how AI affects knowledge worker productivity, with important implications for wider adoption and use of AI among knowledge workers.

April 3 – Detmar Straub to present “A Dark Future for AI: The Looming Spectre of SkyNet?”

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A Dark Future for AI: The Looming Spectre of SkyNet?

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Detmar W. Straub

Professor and IBIT Research Fellow

Temple University Fox School of Business

Regents Professor Emeritus

University System of Georgia and Georgia State University

Friday, April 3

10:30 – 12:00 pm | Zoom

Abstact:

Capabilities of AI and thinking/learning machines are clearly overtaking human abilities (a.k.a. “technological singularity” or, more plainly speaking, “singularity”), with several forecasters like Winograd (2006) predicting that machine will outthink us within the first half of the 21st century. Is it possible that humans will not be able to control the burgeoning intelligence of machines and that we will, frighteningly, be subordinated to them, especially as they become self-aware? This talk starts by sketching out some past and present forecasts of when technological singularity will be real and present, what social, economic, and political issues will emerge, what security issues will loom, and finally how futurists (including science fiction writers and the movies) have envisioned the role of human beings in the coming era of the thinking machine. While the future of humanity might be hanging in the balance, one key academic question arises. What should researchers, in particular information systems researchers, study w/r/t AI? This overall issue has been framed as IA versus AI, or intelligence (human) augmentation (IA) versus artificial (computer) intelligence (AI). Enduring research questions might include: (1) technical issues with achieving singularity and requirements such as designing a tamper-proof “kill” switch for intelligent machines; (2) behavioral questions such as the pace of change and problems with duplicating human creativity; (3) social-economic conundrums such what will people do in an era of omnipresent thinking/working machines and worldwide societal disruption; and (4) organizational matters such as will there be an IS/IT Dept. and, if so, what will it do?

April 24 – Xueming Luo to present “Quantifying the Impact of Human-AI Supervisor Assemblages on Employee Performance: A Field Experiment”

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Quantifying the Impact of Human-AI Supervisor Assemblages on Employee Performance: A Field Experiment

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Xueming Luo

Founder/Director of Global Center on Big Data in Mobile Analytics
Charles Gilliland Distinguish
Chair Professor of Marketing, Strategy, and MIS
Fox School of Business
Temple University

Friday, April 24

10:30 – 12:00 pm | Zoom

Abstact:

Despite the promises of artificial intelligence (AI), there are concerns from both employees and managers about adopting AI at workplaces. Examining how firms can integrate AI into performance management systems (PMS), this research focuses on the impact of various human-AI supervisor assemblages on employees’ task performances and relations with human bosses. We utilize data from a field experiment on customer service employees in a fintech company who are randomly assigned to receive job performance feedback from human managers only, an AI bot only, or human-AI supervisory assemblages. A unique feature in our experiment is that the assemblages encompass a dual human-and-AI configuration (where employees receive feedback from both human managers and an AI bot in parallel) and a shadow-AI-human-face configuration (where employees receive feedback that is generated by an AI bot but delivered by human managers). The results suggest that relative to conventional human supervision, a dual human-and-AI design negatively impacts employee task performance, whereas a shadow-AI-human-face design positively impacts employee task performance. Explorations of the mechanisms support that a dual condition with both AI and human supervision in parallel leads employees to perceive more confused leadership and feedback, less learning from the feedback, and lower employee-manager relationship quality in a vicious cycle. In contrast, the shadow-AI design significantly improves employees’ perceptions of feedback accuracy and consistency, willingness to proactively seek feedback, and organizational commitment in a virtuous cycle. These findings suggest that firms should prudently design the human-AI supervisory assemblages. As a double-edged sword, AI-based PMS should be deployed in the shadows to empower human managers, rather than to displace or compete with them, to achieve higher worker productivity and healthier employee-manager relationships.

Feb 21 – Omar El-Sawy to present “Humanistic Orchestration of Artificial Intelligence Applications in Real-Time Business Platform Contexts: A Rhythmic Fabric Theory”

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Humanistic Orchestration of Artificial Intelligence Applications in Real-Time Business Platform Contexts: A Rhythmic Fabric Theory

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Omar El-Sawy

Kenneth King Stonier Professor of Business Administration
Professor of Data Sciences and Operations Department
Marshall School of Business
University of Southern California

Friday, February 21

10:30 – 12:00 pm | Speakman 200

Abstact:

AI applications are increasingly being deployed in real-time business platform contexts in which tempo is very fast and digital connectivity is very high. This creates a number of new challenges that go beyond the typical challenges and perspective of the deployment of digital technologies, as AI applications come with a different level of engagement and learning that require more complex orchestration. This presentation develops and exposits the elements of a theory for the humanistic orchestration of AI applications in real-time management business platform contexts that is based on rhythms.

We use an abductive theory building approach that moves from empirical observation to inductive insights to empirical diagnosis to theory development. Our recent real-time management study (Rydén & El Sawy, “How Managers Perceive Real-Time Management: Thinking Fast & Flow”, California Management Review, Feb 2019) has uncovered a phenomenon that is key to humanistic engagement in real-time contexts that we have called Fast & Flow. In this presentation, we draw on a case study of a renter’s insurance AI-enabled application (Lemonade) to couple AI engagement with Fast & Flow behavior. We argue that a rhythmic perspective using Fast & Flow is more appropriate in real-time business platform settings, and we develop a rhythmic fabric ontology for AI engagement. Using this rhythmic perspective, we build elemental propositions of a rhythmic theory for humanistic AI orchestration in real-time business platform settings. We use another case study context (Microsoft Outlook) to illustrate the rhythmic theory in action and make recommendations for the way forward for AI orchestration in organizations. Implications for information systems researchers and management researchers are provided.

(work with Pernille Rydén, Danish Technical University)