MIS Distinguished Speaker Series

Temple University

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Mar 24 – Shaila Miranda – “Setting an IT Innovation Agenda: The Practice Repertoire of Bots in a Blockchain Discourse”

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Time: Friday, 24 March 2023, 10:30–12:00
Room: LW420

Abstract

Communities make sense of social issues through discourse. An “issue” is a matter of potential concern. Issues can involve public policy or innovations. Issues do not exist prior to a discourse, but rather are the product of sensemaking and social construction through discourse. This constitutive nature of community discourse has been noted for information technology (IT) innovation. Through discourse, actors learn vicariously about the innovation, without needing to invest in it. Through discourse, actors advance diverse frames about the innovation, advocating for competing innovations or versions of an innovation – or even subverting the innovation. Prior research has highlighted the distinctive role of mass media in drawing attention to social issues and filtering information about them to shape public opinion. Discourse now takes place on digital mass media, where social bots abound. Though researchers have noted the role played by such bots in other venues, we lack understanding of the role they play in IT innovation discourses. Our study therefore asks: How do social bots participate in an IT innovation discourse? To address this question, we studied seven years of a Twitter blockchain discourse. Because our aim was to isolate the distinctive role of bots, we limited our investigation to discourse occurring in a single geographical area – Australia – to reduce confounds by cultural factors. Using text mining in a computational theory construction approach, we observed social bots to evince three sets of practices: innovation spotlighting, innovation framing, and innovation visibilizing practices. We theorize how this practice repertoire shapes an innovation discourse, i.e., by contributing to setting the agenda for the IT innovation. As the number of social bots grows, understanding how they shape innovation discourses will be essential to key innovation stakeholders and policymakers.

Bio

Shaila M. Miranda is the W.P. Wood Professor of MIS at the Price College of Business, the University of Oklahoma. She has a doctorate in Management Information Systems from the University of Georgia and an M.A. in Sociology from Columbia University. Her research focuses primarily on public discourse and shared meaning in the arenas of digital activism and innovation. She employs a combination of qualitative and computational inductive techniques. Shaila has published a book, Social Analytics, through Prospect Press and her research has appeared in journals such as the MIS Quarterly, Information Systems Research, Journal of Management Information Systems, Small Group Research, Information and Management, and Data Base. She serves as Senior Editor for MIS Quarterly and previously has served as Senior Editor for Information Systems Research.

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.