@article {9998,
	title = {Functional and Visionary Leadership in Self-Managing Virtual Teams},
	journal = {Group \& Organization Management},
	volume = {46},
	year = {2021},
	month = {04/2021},
	pages = {424{\textendash}460},
	abstract = {<p>In this conceptual article, we present a theory of leadership in self-managing virtual teams. We describe leadership in this setting as a process that results in the creation, reinforcement and evolution of shared mental models and shared norms that influence team member behaviour towards the successful accomplishment of shared goals. We distinguish two types of leadership. We identify leadership that works within and reinforces existing models and norms to influence team contributions as {\textquotedblleft}functional{\textquotedblright} leadership. We identify leadership that results in changes in models and norms as {\textquotedblleft}visionary{\textquotedblright} leadership. We propose that successful self-managing virtual teams require both types of leadership and that they will exhibit a paradoxical combination of shared, distributed functional leadership complemented by strong, concentrated and centralized visionary leadership and that visionary leadership is enabled by functional leadership in the form of substantive team member contributions.</p>},
	keywords = {functional leadership, Leadership, Self-managing teams, structuration theory, virtual teams, visionary leadership},
	doi = {10.1177/1059601120955034},
	attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/1059601120955034_0.pdf},
	author = {Eseryel, U. Yeliz and Kevin Crowston and Heckman, Robert}
}
@proceedings {9999,
	title = {Helping data science students develop task modularity},
	year = {2019},
	abstract = {<p>This paper explores the skills needed to be a data scientist. Specifically, we report on a mixed method study of a project-based data science class, where we evaluated student effectiveness with respect to dividing a project into appropriately sized modular tasks, which we termed task modularity. Our results suggest that while data science students can appreciate the value of task modularity, they struggle to achieve effective task modularity. As a first step, based our study, we identified six task decomposition best practices. However, these best practices do not fully address this gap of how to enable data science students to effectively use task modularity. We note that while computer science/information system programs typically teach modularity (e.g., the decomposition process and abstraction), and there remains a need identify a corresponding model to that used for computer science / information system students, to teach modularity to data science students.</p>},
	keywords = {data science, modularity, Stigmergy},
	doi = {10.24251/HICSS.2019.134},
	url = {http://hdl.handle.net/10125/59549},
	attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/modularity-HICSS-final-afterReview.pdf},
	author = {Jeffery Saltz and Heckman, Robert and Kevin Crowston and Sangseok You and Yatish Hegde}
}
