Determining causal relationships in leadership research using Machine Learning: The powerful synergy of experiments and data science

Generative AI enhances individual creativity but reduces the collective diversity of novel content

Creativity is core to being human. Generative artificial intelligence (AI)-including powerful large language models (LLMs)-holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on generative AI ideas. We study the causal impact of generative AI ideas on the production of short stories in an online experiment where some writers obtained story ideas from an LLM. We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, generative AI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: With generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced. Our results have implications for researchers, policy-makers, and practitioners interested in bolstering creativity.

More than Follow the Leader: Expectations, Behaviors, Stability, and Change in a Co-Created Leadership Process

This conceptual paper challenges assumptions about the primacy of leaders and leading over followers and following in the leadership process. Leadership cannot be holistically understood unless followership and leadership are researched as they are enacted—in tandem. To move toward a more complete understanding of leadership, we introduce a leadership system which involves a co-created leadership process, unfolds over time, and accounts for stability and change in partners’ expectations and behaviors. Drawing on role theory and implicit leadership (ILTs) and followership (IFTs) theories, we suggest how expectations of self and other influence leading and following behaviors and the leadership process. Personal learning acquired through the experience of leadership results in stability or change in one’s future expectations. Our framework explains how stability and change in each dyad partners’ leadership and followership expectations comes about through constructionist and constructivist mechanisms. These mechanisms occur across two timelines: The first is the microadjustments made to expectations and behaviors within a particular leadership occurrence. The second is the loop between personal learning and the expectations each member carries into future leadership occurrences and relationships. Practical implications arising from this new framework include considerations for leadership and followership development and a contribution to leadership forecasting.