The Management Lab Uri Gal & Sean Hansen

AI is dramatically transforming the ways in which we create content, present information, and formulate arguments.
Technologies such as large language models, social media algorithms, and synthetic media generation have the potential to upend mechanisms of social discourse that are central to the functioning of liberal democratic systems.
In this episode of The Management Lab, we explore the impacts of AI on social discourse. We explore a wide range of questions, including the following:
What specific technologies have the greatest potential to affect social discourse?
How can we discriminate between truth and falsehood and what is the importance of critical thinking in an AI era?
What effects might AI have for organizations and managers?
Are there potential benefits for AI in social discourse?
What can we do to address the threats to social discourse engendered by AI?
This discussion is a bit broader that our usual focus, but tune in to learn more about AI and its societal impact.
 
Research discussed in the episode:
Brady, W. J., Jackson, J. C., Lindström, B., & Crockett, M. J. (2023). Algorithm-mediated social learning in online social networks. Trends in Cognitive Sciences, 27(10), 947-960.
Brandt, J. (2023). Propaganda, foreign interference, and generative AI. Washington, DC: The Brookings Institution.
Chesney, B., & Citron, D. (2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107, 1753.
Goldstein, J. A., Chao, J., Grossman, S., Stamos, A., & Tomz, M. (2024). How persuasive is AI-generated propaganda? PNAS Nexus, 3(2), 034.
Hazell, J. (2023). Spear phishing with large language models. arXiv Preprint arXiv:2305.06972.

The use of AI for mental support is rapidly increasing. Research shows that AI designed to understand and respond to human emotions can help people combat depression, avoid suicide, and improve their human relationships. 
We examine both the potential and ethical questions arising from using AI for mental support. We also discuss how using such technologies can create new opportunities and challenges for organizations.
Sources Discussed:
Ayers, J.W., et al. (2023). Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Internal Medicine, 183(6), 589-596.
Li, J. Z., Herderich, A., & Goldenberg, A. (2024). Skill but not Effort Drive GPT Overperformance over Humans in Cognitive Reframing of Negative Scenarios. PsyArXiv Preprints. URL: https://doi.org/10.31234/osf.io/fzvd8
Maples, B., Cerit, M., Vishwanath, A., & Pea, R. (2024). Loneliness and suicide mitigation for students using GPT3-enabled chatbots. npj Mental Health Research, 3(1), 4.
Sharma, A., Lin, I. W., Miner, A. S., Atkins, D. C., & Althoff, T. (2023). Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nature Machine Intelligence, 5(1), 46-57.

Techno-stress refers to the stress we feel because of our inability to adjust to the introduction of new technologies. It can reduce workers’ job satisfaction, increase attrition, and hinder organizations’ ability to innovate. Sean and Uri examine the science of the causes, nature, and implications of technostress, and how managers can mitigate its negative effects.
Research discussed in the episode:
Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831-858.
Califf, C. B., Sarker, S., & Sarker, S. (2020). The bright and dark sides of technostress: A mixed-methods study involving healthcare IT. MIS Quarterly, 44(2), 809-856.
Nastjuk, I., Trang, S., Grummeck-Braamt, J. V., Adam, M. T., & Tarafdar, M. (2023). Integrating and synthesising technostress research: a meta-analysis on technostress creators, outcomes, and IS usage contexts. European Journal of Information Systems, 1-22.
Tarafdar, M., Pullins, E. B., & Ragu‐Nathan, T. S. (2015). Technostress: negative effect on performance and possible mitigations. Information Systems Journal, 25(2), 103-132.

Workplace boredom refers to a state of low arousal and dissatisfaction, which is attributed to an inadequately stimulating work environment. It can lead to counterproductive behavior, job turnover, anxiety, and depression. Sean and Uri examine the science of the causes, nature, and implications of workplace boredom, and how managers can make it more interesting.

With the launch of the Apple Vision Pro, excitement about the potential of virtual reality (VR) and augmented reality (AR) is running hot. In this episode of The Management Lab, we explore these emergent technologies along with their most promising business applications and the challenges to their organizational use.
 
Research we discuss in the episode:
Adhyaru, J. S., & Kemp, C. (2022). Virtual reality as a tool to promote wellbeing in the workplace. Digital Health, 8, 2055-2076.
Berman, B., & Pollack, D. (2021). Strategies for the successful implementation of augmented reality. Business Horizons, 64(5), 621-630.
de Regt, A., Barnes, S. J., & Plangger, K. (2020). The virtual reality value chain. Business Horizons, 63(6), 737-748.
Porter, M. E., & Heppelmann, J. E. (2017). Why every organization needs an augmented reality strategy. Harvard Business Review, 95(6), 46-57.
Riches, S., Taylor, L., Jeyarajaguru, P., Veling, W., & Valmaggia, L. (2023). Virtual reality and immersive technologies to promote workplace wellbeing: a systematic review. Journal of Mental Health, 1-21.
Venkatesan, M., Mohan, H., Ryan, J. R., Schürch, C. M., Nolan, G. P., Frakes, D. H., & Coskun, A. F. (2021). Virtual and augmented reality for biomedical applications. Cell Reports Medicine, 2(7), 100348.

Stereotype threat refers to the performance-sapping impact of situations in which people feel they are at risk of confirming or reinforcing negative stereotypes about a social group of which they are a member. Sean and Uri investigate the science of the causes and nature of stereotype threat, and how managers can mitigate its effects.