April, 20th 2023

Thursday

Jeffrey Nickerson
(Stevens Institute of Technoloty)

Title: AI Blends

Abstract: AI tools, including language and image models,  can be combined with hardware and software tools as well as with human minds and bodies. Recent studies based on language and image models used in journalism will provide a background for a discussion of the design space of apps that combine human and machine processes, including examples drawn from research on video games, simulations, and autonomous driving. 

May, 2nd 2023

Tuesday

Title: Does Editorial Discretion Improve on Reviewer Recommendations in Journal Peer Review?

Abstract: Editors at most peer-reviewed journals have the authority to overturn reviewer recommendations and discretionarily reject or accept submissions. But does editorial discretion improve on reviewer recommendations? Using data from Manufacturing & Service Operations Management, we find that editorial discretion in general does not improve on reviewers in terms of post-publication impact. Submissions discretionarily accepted or invited for revision are not more impactful than reviewer-rejected submissions and most discretionarily rejected submissions are not less impactful than reviewer-accepted or invited submissions. While editorial discretion leads to a decrease in Type I errors (incorrect acceptances or invitations to revise), it comes at the cost of more Type II errors (incorrect rejections). In contrast, reviewer recommendations significantly predict future impact, especially for very high-impact submissions.

May, 16th 2023

Tuesday

Title: The technology-behavioral compensation effect: Unintended consequences of health technology adoption

Abstract: In their pursuit of public health goals, policymakers increasingly turn to innovative apps as complements to existing health measures. Nevertheless, findings from previous research in non-IT health contexts indicate that individuals may compensate for new interventions (e.g., start exercising) by reducing existing preventive health behaviors (e.g., eating less healthy foods). However, the findings are inconclusive, and it is unknown when people tend to engage in behavioral compensation. Building on this observation, we draw on rational choice theory to substantiate the subjective rationality of compensation behavior and develop a utility maximization model that suggests circumstances under which the adoption of technological innovation may lead to users reducing existing preventive health behaviors. This research provides evidence from a multi-wave study on COVID-19 contact-tracing apps that confirms the existence of what we term the technology-behavioral compensation effect: Individuals who perceive the app to be highly useful or actively use it reduce other preventive health behaviors (e.g., social distancing) after app adoption. Ironically, this technology-behavioral compensation effect indicates a hitherto-overlooked tension between two established IS design goals (i.e., perceived usefulness and active use) and the successful exploitation of technology to support users’ health. We expand research on dark side effects of IS use by revealing a previously neglected type of unintended consequence and elaborate on its implications for research well beyond the health context.

June, 6th 2023

Tuesday

Title: tbd

Abstract: tbd

June, 15th 2023

Thursday

Title: tbd

Abstract: tbd

June, 20th 2023

Tuesday

Title: tbd

Abstract: tbd

July, 4th 2023

Tuesday

Title: tbd

Abstract: tbd

July, 18th 2023

Tuesday

Title: tbd

Abstract: tbd