TY - Generic T1 - AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models T2 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems Y1 - 2023 A1 - Petridis, Savvas A1 - Diakopoulos, Nicholas A1 - Crowston, Kevin A1 - Hansen, Mark A1 - Henderson, Keren A1 - Jastrzebski, Stan A1 - Nickerson, Jefrey V A1 - Chilton, Lydia B JF - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems UR - https://savvaspetridis.github.io/papers/anglekindling.pdf ER - TY - JOUR T1 - Perceived benefits of open data are improving but scientists still lack resources, skills, and rewards JF - Humanities and Social Sciences Communications Y1 - 2023 A1 - Borycz, Joshua A1 - Olendorf, Robert A1 - Specht, Alison A1 - Grant, Bruce A1 - Crowston, Kevin A1 - Tenopir, Carol A1 - Allard, Suzie A1 - Rice, Natalie M. A1 - Hu, Rachael A1 - Sandusky, Robert J. VL - 10 IS - 1 ER - TY - JOUR T1 - Hybrid intelligence in business networks JF - Electronic Markets Y1 - 2021 A1 - Ebel, Philipp A1 - Söllner, Matthias A1 - Leimeister, Jan Marco A1 - Crowston, Kevin A1 - de Vreede, Gert-Jan ER - TY - JOUR T1 - GitLab: Work where you want, when you want JF - Journal of Organizational Design Y1 - 2020 A1 - Choudhury, Prithwiraj A1 - Crowston, Kevin A1 - Dahlander, Linus A1 - Minervini, Marco S. A1 - Raghuram, Sumita AB -

GitLab is a software company that works “all remote” at the scale of more than 1000 employees located in more than 60 countries. GitLab has no physical office and its employees can work from anywhere they choose. Any step of the organizational life of a GitLab employee (e.g., hiring, onboarding and firing) is performed remotely, except for a yearly companywide gathering. GitLab strongly relies on asynchronous coordination, allowing employees to work anytime they want. After highlighting some of the main practices implemented by GitLab to effectively work all remotely and asynchronously, I asked renowned organizational scientists their thoughts on this interesting case and to question the generalizability of the all remote asynchronous model. Understanding whether and under what conditions this model can succeed can be of guidance for organizational designers that are now considering different remote models in response of the COVID-19 shock and its aftermath.

VL - 9 SN - 2245-408X IS - 1 ER - TY - CHAP T1 - Comprehensive collaboration plans: Practical considerations spanning across individual collaborators to institutional supports T2 - Strategies for Team Science Success Y1 - 2019 A1 - Hall, Kara L. A1 - Vogel, Amanda L. A1 - Crowston, Kevin ED - Hall, Kara L. ED - Vogel, Amanda L. ED - Croyle, Robert T. AB - This chapter provides a framework for integrating and applying the principles and strategies for effective team science that are described in this volume. The framework, called Collaboration Planning, aims to guide a deliberative approach to assess and plan for ten key influences on both scientific and collaborative success. These influences range from the initial scientific rationale for a team science approach to the collaboration readiness of participating individuals and institutions to team communication and coordination mechanisms to quality improvement for team functioning. The Collaboration Planning framework guides current or future collaborators through dialogue and planning around each influence. It draws their attention to key issues for consideration related to each influence, and facilitates discussion of how to leverage facilitating factors and plan for, or mitigate, challenges. Decisions are captured in a resulting written document called the Collaboration Plan. The Collaboration Plan summarizes the various ways the team plans to build the foundation for, and support, effective collaboration across the lifespan of the team science initiative. Collaboration Plans can be used in multiple ways. The Plans' core function is as a roadmap to facilitate effective team formation and functioning. The Plan also can be used for benchmarking or guiding quality improvement-oriented evaluation. Collaboration Plans also can be used to communicate a team’s likelihood of collaborative success, goals, and needs to a wide variety of audiences, including funders, current and future team members, stakeholders in the team’s success, and organizational leaders. In addition, they can be used as models to guide future teams in laying the foundation for success. JF - Strategies for Team Science Success PB - Springer International Publishing CY - Cham SN - 978-3-030-20990-2 ER - TY - CHAP T1 - Lessons Learned from a Decade of FLOSS Data Collection T2 - Big Data Factories Y1 - 2017 A1 - Crowston, Kevin A1 - Squire, Megan ED - Matei, Sorin Adam ED - Jullien, Nicolas ED - Goggins, Sean P. JF - Big Data Factories PB - Springer International Publishing CY - Cham SN - 978-3-319-59185-8 UR - http://link.springer.com/content/pdf/10.1007/978-3-319-59186-5 ER -