The Management Lab

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.

Employee feedback is an essential component of work life that can be a source of individual growth and organizational success but also of anxiety and rigidity. Sean and Uri investigate the science of how managers can deliver effective feedback.

Employee voice refers to the ways in which employees communicate their opinions and suggestions to their managers on how to improve their organization. Conversely, employee silence occurs when employees withhold their views due to fear of negative consequences or a belief that their opinions are not valued. Understanding these dynamics is critical for managers because encouraging employee voice can lead to better organizational outcomes, such as higher performance, innovation, and employee retention. On the other hand, addressing the reasons behind employee silence can prevent potential issues from escalating and improve the overall workplace environment. In this episode, we delve into the research on employee voice and silence and address some of its main questions: What are the primary drivers of voice and silence? What are the effects of voice and silence behavior on individual employees and organizations? What can organizational leaders do to encourage voice and decrease silence behavior? How can we design organizational structures and processes to encourage voice and decrease silence behavior? Do men and women differ in their propensity for voice/silence behavior?

In our rapidly-evolving, digitally-dominated world, many of us seek meaning, purpose, and a deeper connection to our inner self. This quest is not confined to our personal lives; it extends to our professional environments as well. Spirituality, therefore, is emerging as a vital component for organizational success and employee well-being. In this episode, Sean and Uri discuss spirituality in the workplace and unpack why spiritual practices and principles are no longer just personal pursuits but essential organizational strategies. Some of the topics covered include: What is spirituality and how it relates to religious beliefs. The difference between personal and organizational spirituality. How spirituality in the workplace can positively affect employees’ cognition, emotion, and behavior, ultimately driving organizational performance. The potential of promoting mindful practices to enhance employee empathy, leadership skills, and resilience. In our discussion, we draw on the following research: Anderson & Burchell (2021) The effects of spirituality and moral intensity on ethical business decisions: A Cross-Sectional Study. Journal of Business Ethics. Good et al (2016) Contemplating Mindfulness at Work: An Integrative Review. Journal of Management Harris (2014) Waking up: A guide to spirituality without religion. Kolodinsky et al (2008) Workplace Values and Outcomes - Exploring Personal, Organizational, and Interactive Workplace Spirituality. Journal of Business Ethics. Pourmola et al (2019) Investigating the impact of organizational spirituality on human resources productivity. Management Science Letters.

The explosion of generative AI in the past year has engendered plenty of emotional responses across the globe. Not surprisingly, the interaction of emotions and AI is a critical facet of our adaptation to this technology. In this episode, we discuss the intersection between AI and human emotions, how AI can recognize and respond to emotion in users, and how human emotions can be leveraged to increase the use of AI. Specific question we tackle include: What makes emotional artificial intelligence distinctive from other emerging AI technologies? To what degree can cultural and contextual factors be accounted for by emotional artificial intelligence? What do people perceive to be the benefits and challenges of emotional artificial intelligence? What issues does the use of EAI pose for personal privacy and autonomy? Can AI devices function as a viable replacement for human companionship?  

Over the past decade, businesses have made a concerted effort to improve their monitoring, analysis, and reporting of a wide range of non-monetary societal impacts collectively grouped under the label of Environmental, Social, and Governance (ESG) factors. Driven largely by the investment community, the ESG framework seeks to capture diverse, traditionally-overlooked aspects of organizational performance. In this episode of The Management Lab, we explore this emergent concept with key insights from some of the latest research on the topic. Specific topics discussed include: What organizational activities fall under the ESG rubric? How does ESG differ from the earlier movement of corporate social responsibilities (CSR)? What does the evidence suggest regarding ESG efforts’ impacts on a firm’s overall performance? How much do investors really care about the ESG activities of a firm? Do current measures of ESG performance really get at meaningful impacts on society? Give it a listen and see what you make of the ESG phenomenon. Are you a true believer, a persistent skeptic, or somewhere in between?

Along with most other aspects of our business environment, the domain of human resource management is being fundamentally reshaped through the application of advanced data analytics. People analytics is a class of technology tools aimed at promoting evidence-based practices in organizations and allowing managers to make objective and rational decisions about their workforce. In this episode of The Management Lab, we explore the promise and perils of people analytics, and discuss these topics: The assumptions that underlie the vision of people analytics. What is people analytics good for and what applications may reflect an overreach of the technology? The key drivers and impediments for effective implementation and use of people analytics in organizations. The interaction of organizational culture and people analytics effectiveness. The potential for the reinforcement of biases in the use of people analytics Have a listen and see what you make of this emergent class of technology resources.

We're diving into the transformative role of generative AI in the workplace and examine if and how it impacts business productivity. Some of the questions we explore are: Should managers be unleashing the power of generative AI on everything in sight, or should they take a more measured approach? Does ChatGPT merely augment human capabilities or is destined to replace us? Is there still a role for human intuition in the AI era? And, can Sean do mental math?   In our discussion, we draw on the following studies: Brynjolfsson, E., Li, D., & Raymond, L.R. (2023). Generative AI at work (Working Paper 31161). National Bureau of Economic Research. Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11, 1–25. Deranty, J.-P., & Corbin, T. (2022). Artificial Intelligence and work: A critical review of recent research from the social sciences. AI & SOCIETY, 1–17. Enholm, I.M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2022). Cognitive challenges in Human–Artificial Intelligence Collaboration: Investigating the path toward productive delegation. Information Systems Research, 33(2), 678–696. Jarrahi, M.H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. SSRN. https://ssrn.com/abstract=4375283 Ransbotham, S., Khodabandeh, S., Kiron, D., Candelon, F., Chu, M., & LaFountain, B. (2020). Expanding AI’s impact with organizational learning. MIS Sloan Management Review – Research Report.  

We finally break the seal on a discussion of generative artificial intelligence (AI). Since the launch of ChatGPT at the end of 2022, the potential of generative AI for good and for ill has dominated technology speculation across the globe. In this episode, we explore several of the ethical dimensions of generative AI (while conceding that such dimensions are almost unlimited). Specific topics discussed include: The evidence of political bias in generative AI systems The inconsistent moral argumentation of generative AI systems Can generative AI take the place of human actors in cognitive science research (and would we want it to)? Is the potential for personalized persuasion via generative AI a boon for the field of marketing or a dangerous path toward societal thought control? Is there such a thing as a non-Irish Limerick?   Research discussed includes the following studies: Dillion, D., Tandon, N., Gu, Y., & Gray, K. (2023). Can AI language models replace human participants? Trends in Cognitive Sciences. Krügel, S., Ostermaier, A., & Uhl, M. (2023). ChatGPT’s inconsistent moral advice influences users’ judgment. Scientific Reports, 13(1), 1–5. Matz, S., Teeny, J., Vaid, S. S., Harari, G. M., & Cerf, M. (2023). The Potential of Generative AI for Personalized Persuasion at Scale. PsyArXiv; PsyArXiv. McGee, R. W. (2023). Is ChatGPT biased against conservatives? An empirical study. SSRN. Rozado, D. (2023). The Political Biases of ChatGPT. Social Sciences, 12(3), 148.

CEOs are central figures in organizations, standing at the helm of large corporations and wielding the power to create substantial wealth. Their influence is imprinted not just on their employees and shareholders, but also on society at large. They create jobs, foster innovation, and drive the engine of our economy. On the other hand, some CEOs prioritize short-term financial gains over long-term corporate health, employee welfare, and societal benefits. CEOs’ power can lead to unchecked decision-making, risking not only company assets but also affecting the lives of employees and stakeholders. In this episode, we explore the degree to which CEOs actually influence the performance of the organizations that they lead. We draw on recent research to answer some of the most pressing questions in this area, including: What CEO leadership styles most strongly contribute to firm performance? Does delegating tasks to subordinates contribute to firm performance? In which countries are senior leaders most likely to delegate tasks? Does a transformational leadership style improve firm performance? How is prior CEO experience associated with firm performance? What type of CEO is most likely to take time off when popular sporting events are being broadcast? Research discussed in this episode include the following: Ashford, S. J., Wellman, N., Sully de Luque, M., De Stobbeleir, K. E., & Wollan, M. (2018). Two roads to effectiveness: CEO feedback seeking, vision articulation, and firm performance. Journal of Organizational Behavior, 39(1), 82–95. Hamori, M., & Koyuncu, B. (2015). Experience matters? The impact of prior CEO experience on firm performance. Human Resource Management, 54(1), 23–44. Jensen, M., Potočnik, K., & Chaudhry, S. (2020). A mixed-methods study of CEO transformational leadership and firm performance. European Management Journal, 38(6), 836–845. Mackey, A. (2008). The effect of CEOs on firm performance. Strategic Management Journal, 29(12), 1357–1367. Sadun, R. (2023). CEOs and Firm Performance. e, 1, Cambridge, MA: National Bureau of Economic Research.

Recent decades provide ample evidence of the potential for unethical behavior in organizational leadership. From Enron to Volkswagen and Wells Fargo to Purdue Pharma, we see that ethical failings in managerial ranks can have devastating effects on organizations and society at large. In this episode, we explore some research on the dynamics of leadership personality and its implications for ethical behavior (or lack thereof). We preface our discussion with an overview of the five-factor model of personality (the so-called Big Five), which looms large over this research domain. From there, we probe multiple dimensions of the phenomenon: Personality traits that are most associated with ethical leadership behavior Differences in ethicality across distinct leadership styles  Red flags for the potential of ethical failings in leaders The damage that can be done by narcissistic leaders As always, we glean actionable insights for managers and those charged with identifying potential leaders in organizations. Studies discussed in this episode include the following: De Vries, R. E. (2012). Personality predictors of leadership styles and the self–other agreement problem. The Leadership Quarterly, 23(5), 809–821. Kalshoven, K., Den Hartog, D. N., & De Hoogh, A. H. (2011). Ethical leader behavior and big five factors of personality. Journal of Business Ethics, 100, 349–366. O’Reilly III, C. A., Chatman, J. A., & Doerr, B. (2021). When “me” trumps “we”: Narcissistic leaders and the cultures they create. Academy of Management Discoveries, 7(3), 419–450. Sosik, J. J., Gentry, W. A., & Chun, J. U. (2012). The value of virtue in the upper echelons: A multisource examination of executive character strengths and performance. The Leadership Quarterly, 23(3), 367–382. Van Scotter, J. R., & Roglio, K. D. D. (2020). CEO bright and dark personality: Effects on ethical misconduct. Journal of Business Ethics, 164, 451–475.

How can managers harness behavioral science to help their employees make better decisions? The principle of nudging says that, by tapping into innate psychological tendencies, we can design choice scenarios to increase the likelihood of individuals choosing well. In this episode we delve into the world of nudging techniques, examining how they can influence decision-making in various organizational scenarios, with a special focus on the ethical dimensions of the practice. Some of the topics we cover are: Does nudging actually work [spoiler: yes]? Can nudging be taken too far? If so, what are the limiting conditions for effective (and ethical) nudging? What is the difference between a “nudge” and a “boost”? Are data visualization and marketing just elaborate forms of nudging? What on earth are “choice architectures” and “libertarian paternalism” anyway? Research discussed in this episode include the following studies: Beshears, J., & Kosowsky, H. (2020). Nudging: Progress to date and future directions. Organizational Behavior and Human Decision Processes, 161, 3–19. Hertwig, R., & Grüne-Yanoff, T. (2017). Nudging and boosting: Steering or empowering good decisions. Perspectives on Psychological Science, 12(6), 973–986. Reeck, C., Posner, N. A., Mrkva, K., & Johnson, E. J. (2023). Nudging App Adoption: Choice Architecture Facilitates Consumer Uptake of Mobile Apps. Journal of Marketing, 1–18. Rozeboom, G. J. (2021). How to Evaluate Managerial Nudges. Journal of Business Ethics, 182, 1073–1086. Schmidt, A. T., & Engelen, B. (2020). The Ethics of Nudging: An Overview. Philosophy Compass, 15(4), e12658.

As more members of Gen Z enter the workforce, organizations need to understand how to effectively manage this new cohort of employees. In this episode, we discuss the challenges that managers may face when managing Gen Z employees and provide strategies for how to effectively manage and motivate them. Some of the topics we will cover are: What factors differentiate Gen Z from previous generations of employees? What do Gen Zers look for in their ideal workplace? How can organizations effectively manage inter-generational conflicts? What leadership styles are best suited to manage Gen Zers? The papers we discuss are: Applebaum et al (2022) A Study of Generational Conflicts in the Workplace. European Journal of Business and Management Research. Schroth (2019) Are you Ready for Gen Z in the Workplace? California Management Review. Pichler et al (2021) DITTO for Gen Z: A framework for leveraging the uniqueness of the new generation. Business Horizons. Gabrielova & Buchko (2021) Here comes Generation Z: Millennials as managers. Business Horizons. Grow & Yang (2018) Generation-Z Enters the Advertising Workplace: Expectations Through a Gendered Lens. Journal of Advertising Education. Maloni et al (2019) Understanding the work values of Gen Z business students. The International Journal of Management Education.  

In this episode, we’re discussing the rise of remote work and how companies and employees are adapting to this new way of working. We'll examine current academic research to explore the benefits and challenges of remote work, and provide insights and tips for companies and individuals looking to make the most of this new era of work.   Some of the questions we’ll answer are: Does working from home increase job satisfaction and productivity? How is being on camera during online meetings affect employee burnout? Are women particularly vulnerable to the negative effects of WFH? How does having a remote workforce impact organizations’ ability to innovate?   Papers we discuss in this episode are: Brucks, M. S., & Levav, J. (2022) Virtual communication curbs creative idea generation. Nature, 605(7908), 108–112. Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2022). Does remote work flexibility enhance organization performance? Moderating role of organization policy and top management support. Journal of Business Research, 139, 1501–1512. Leroy, S., Schmidt, A. M., & Madjar, N. (2021). Working from home during COVID-19: A study of the interruption landscape. Journal of Applied Psychology, 106(10), 1448–1465. Shockley, K. M., Allen, T. D., Dodd, H., & Waiwood, A. M. (2021). Remote worker communication during COVID-19: The role of quantity, quality, and supervisor expectation-setting. Journal of Applied Psychology, 106(10), 1466–1482. Shockley, K. M., Gabriel, A. S., Robertson, D., Rosen, C. C., Chawla, N., Ganster, M. L., & Ezerins, M. E (2021). The fatiguing effects of camera use in virtual meetings: A within-person field experiment. Journal of Applied Psychology, 106(8), 1137–1155. Wu, Y., Antone, B., Srinivas, A., DeChurch, L., & Contractor, N. (2021). Teamwork in the time of COVID-19: Creating, dissolving, and reactivating network ties in response to a crisis. Journal of Applied Psychology, 106(10), 1483–1492.

Is your business using AI? Are you thinking of implementing AI into your business? Then you should learn about algorithmic aversion. Join us to find out why people are reluctant to use algorithms and what we can do to build trust in algorithms.   Some of the topics we cover are: How algorithmic aversion can manifest in different contexts, such as healthcare, criminal justice, and financial systems. What are the main causes of algorithmic aversion? Do we distrust algorithms more when they make moral decisions, such as those that directly affect human lives?   The papers we discuss are: Bigman, Y. E., & Gray, K. (2018). People are averse to machines making moral decisions. Cognition, 181, 21–34. Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114. Efendić, E., Van de Calseyde, P. P., & Evans, A. M. (2020). Slow response times undermine trust in algorithmic (but not human) predictions. Organizational Behavior and Human Decision Processes, 157, 103–114. Jago, A. S. (2019). Algorithms and authenticity. Academy of Management Discoveries, 5(1), 38–56. Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103.