  Discovering the antecedents of individuals' influence in collaborative
environments is an important, practical, and challenging problem. In this
paper, we study interpersonal influence in small groups of individuals who
collectively execute a sequence of intellective tasks. We observe that along an
issue sequence with feedback, individuals with higher expertise and social
confidence are accorded higher interpersonal influence. We also observe that
low-performing individuals tend to underestimate their high-performing
teammate's expertise. Based on these observations, we introduce three
hypotheses and present empirical and theoretical support for their validity. We
report empirical evidence on longstanding theories of transactive memory
systems, social comparison, and confidence heuristics on the origins of social
influence. We propose a cognitive dynamical model inspired by these theories to
describe the process by which individuals adjust interpersonal influences over
time. We demonstrate the model's accuracy in predicting individuals' influence
and provide analytical results on its asymptotic behavior for the case with
identically performing individuals. Lastly, we propose a novel approach using
deep neural networks on a pre-trained text embedding model for predicting the
influence of individuals. Using message contents, message times, and individual
correctness collected during tasks, we are able to accurately predict
individuals' self-reported influence over time. Extensive experiments verify
the accuracy of the proposed models compared to baselines such as structural
balance and reflected appraisal model. While the neural networks model is the
most accurate, the dynamical model is the most interpretable for influence
prediction.
