@article {zevin2023gravity, title = {Gravity Spy: Lessons Learned and a Path Forward}, journal = {European Physical Journal Plus}, volume = {139}, year = {2024}, month = {01/2024}, pages = {Article 100}, abstract = {

The Gravity Spy project aims to uncover the origins of glitches, transient bursts of noise that hamper analysis of gravitational-wave data. By using both the work of citizen-science volunteers and machine-learning algorithms, the Gravity Spy project enables reliable classification of glitches. Citizen science and machine learning are intrinsically coupled within the Gravity Spy framework, with machine-learning classifications providing a rapid first-pass classification of the dataset and enabling tiered volunteer training, and volunteer-based classifications verifying the machine classifications, bolstering the machine-learning training set and identifying new morphological classes of glitches. These classifications are now routinely used in studies characterizing the performance of the LIGO gravitational-wave detectors. Providing the volunteers with a training framework that teaches them to classify a wide range of glitches, as well as additional tools to aid their investigations of interesting glitches, empowers them to make discoveries of new classes of glitches. This demonstrates that, when giving suitable support, volunteers can go beyond simple classification tasks to identify new features in data at a level comparable to domain experts. The Gravity Spy project is now providing volunteers with more complicated data that includes auxiliary monitors of the detector to identify the root cause of glitches.

}, doi = {10.1140/epjp/s13360-023-04795-4}, author = {Michael Zevin and Corey B. Jackson and Zoheyr Doctor and Yunan Wu and Carsten {\O}sterlund and L. Clifton Johnson and Christopher P. L. Berry and Kevin Crowston and Scott B. Coughlin and Vicky Kalogera and Sharan Banagiri and Derek Davis and Jane Glanzer and Renzhi Hao and Aggelos K. Katsaggelos and Oli Patane and Jennifer Sanchez and Joshua Smith and Siddharth Soni and Laura Trouille and Marissa Walker and Irina Aerith and Wilfried Domainko and Victor-Georges Baranowski and Gerhard Niklasch and Barbara T{\'e}gl{\'a}s} } @article {9998, title = {Making sense of AI systems development}, journal = {IEEE Transactions on Software Engineering}, volume = {50}, year = {2024}, month = {12/2023}, pages = {123{\textendash}140}, abstract = {We identify and describe episodes of sensemaking around challenges in modern Artificial-Intelligence (AI)-based systems development that emerged in projects carried out by IBM and client companies. All projects used IBM Watson as the development platform for building tailored AI-based solutions to support workers or customers of the client companies. Yet, many of the projects turned out to be significantly more challenging than IBM and its clients had expected. The analysis reveals that project members struggled to establish reliable meanings about the technology, the project, context, and data to act upon. The project members report multiple aspects of the projects that they were not expecting to need to make sense of yet were problematic. Many issues bear upon the current-generation AI{\textquoteright}s inherent characteristics, such as dependency on large data sets and continuous improvement as more data becomes available. Those characteristics increase the complexity of the projects and call for balanced mindfulness to avoid unexpected problems.}, doi = {10.1109/TSE.2023.3338857}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/sensemaking_tse_to_share.pdf}, author = {Mateusz Dolata and Kevin Crowston} } @proceedings {petridis2023anglekindling, title = {AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models}, year = {2023}, doi = {10.1145/3544548.3580907}, url = {https://savvaspetridis.github.io/papers/anglekindling.pdf}, author = {Petridis, Savvas and Diakopoulos, Nicholas and Crowston, Kevin and Hansen, Mark and Henderson, Keren and Jastrzebski, Stan and Nickerson, Jefrey V and Chilton, Lydia B} } @conference {9999, title = {Project archetypes: A blessing and a curse for AI development}, booktitle = {International Conference on Information Systems (ICIS)}, year = {2022}, address = {Copenhagen, Denmark}, abstract = {

Software projects rely on what we propose to call project archetypes, i.e., pre-existing mental images of how projects work. They guide distribution of responsibilities, planning, or expectations. However, with the technological progress, project archetypes may become outdated, ineffective, or counterproductive by impeding more adequate approaches. Understanding archetypes of software development projects is core to leverage their potential. The development of applications using machine learning and artificial intelligence provides a context in which existing archetypes might outdate and need to be questioned, adapted, or replaced. We analyzed 36 interviews from 21 projects between IBM Watson and client companies and identified four project archetypes members initially used to understand the projects. We then derive a new project archetype, cognitive computing project, from the interviews. It can inform future development projects based on AI-development platforms. Project leaders should pro-actively manage project archetypes while researchers should investigate what guides initial understandings of software projects.

}, url = {https://aisel.aisnet.org/icis2022/is_design/is_design/6 }, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/icis_archetypes_rev1_v20_zora.pdf}, author = {Mateusz Dolata and Kevin Crowston and Gerhard Schwabe} } @article {soni2021discovering, title = {Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning}, journal = {Classical and Quantum Gravity}, volume = {38}, year = {2021}, doi = {10.1088/1361-6382/ac1ccb}, author = {S Soni and C P L Berry and S B Coughlin and M Harandi and C B Jackson and K Crowston and C {\O}sterlund and O Patane and A K Katsaggelos and L Trouille and V-G Baranowski and W F Domainko and K Kaminski and M A Lobato Rodriguez and U Marciniak and P Nauta and G Niklasch and R R Rote and B T{\'e}gl{\'a}s and C Unsworth and C Zhang} } @article {9998, title = {Examining Open Innovation in Science (OIS): What Open Innovation can and cannot offer the science of science}, journal = {Innovation: Organization \& Management}, year = {2021}, abstract = {

Scholars across disciplines increasingly hear calls for more open and collaborative approaches to scientific research. The concept of Open Innovation in Science (OIS) provides a framework that integrates dispersed research efforts aiming to understand the antecedents, contingencies, and consequences of applying open and collaborative research practices. While the OIS framework has already been taken up by science of science scholars, its conceptual underpinnings require further specification. In this essay, we critically examine the OIS concept and bring to light two key aspects: 1) how OIS builds upon Open Innovation (OI) research by adopting its attention to boundary-crossing knowledge flows and by adapting other concepts developed and researched in OI to the science context as exemplified by two OIS cases in the area of research funding; 2) how OIS conceptualises knowledge flows across boundaries. While OI typically focuses on well-defined organizational boundaries, we argue that blurry and even invisible boundaries between communities of practice may more strongly constrain flows of knowledge related to openness and collaboration in science. Given the uptake of this concept, this essay brings needed clarity to the meaning of OIS, which has no particular normative orientation toward a close coupling between science and industry. We end by outlining the essay{\textquoteright}s contributions to OI and the science of science, as well as to science practitioners.

}, doi = {10.1080/14479338.2021.1999248}, author = {Susanne Beck and Marcel LaFlamme and Carsten Bergenholtz and Marcel Bogers and Tiare-Maria Brasseur and Marie-Louise Conradsen and Kevin Crowston and Diletta Di Marco and Agnes Effert and Despoina Filiou and Lars Frederiksen and Thomas Gillier and Marc Gruber and Carolin Haeussler and Karin Hoisl and Olga Kokshagina and Maria-Theresa Norn and Marion Poetz and Gernot Pruschak and Laia Pujol Priego and Agnieszka Radziwon and Alexander Ruser and Henry Sauermann and Sonali Shah and Julia Suess-Reyes and Christopher L. Tucci and Philipp Tuertscher and Jane Bj{\o}rn Vedel and Roberto Verganti and Jonathan Wareham and Sunny Mosangzi Xu} } @article {2021, title = {Hybrid intelligence in business networks}, journal = {Electronic Markets}, year = {2021}, month = {Nov-06-2021}, issn = {1019-6781}, doi = {10.1007/s12525-021-00481-4}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/Ebel2021_Article_HybridIntelligenceInBusinessNe.pdf}, author = {Ebel, Philipp and S{\"o}llner, Matthias and Leimeister, Jan Marco and Crowston, Kevin and de Vreede, Gert-Jan} } @unpublished {9999, title = {The Impacts of Automation on Journalists}, year = {2021}, abstract = {We examined journalists{\textquoteright} tasks as described on the Occupational Information Network (O*NET) website. The tasks we analyzed specifically are those assigned to reporters and correspondents (27-3022.00 - Reporters and Correspondents). From the tasks, we developed a workflow diagram for journalist{\textquoteright}s work. Reviewing the previous relevant literature, we made predictions to explore which journalism tasks are more automatable and which are less automatable, and how the trending advent of technologies such automatic content generation may change journalists{\textquoteright} roles. Highlighting research and practical implications, we conclude the paper with limitations and areas for further research. }, author = {Ayse Dalgali and Kevin Crowston} } @conference {2020, title = {Algorithmic Journalism and Its Impacts on Work}, booktitle = {Computation + Journalism Symposium }, year = {2020}, edition = {(cancelled due to COVID; presented in 2021)}, abstract = {

In the artificial intelligence era, algorithmic journalists can produce news reports in natural language from structured data thanks to natural language generation (NLG) algorithms. This paper presents several algorithmic content generation models and discusses the impacts of algorithmic journalism on work within a framework consisting of three levels: replacing tasks of journalists, increasing efficiency, and developing new capabilities within journalism. The findings indicate that algorithmic journalism technology may lead some changes in journalism by enabling individual users to produce their own stories. This paper may contribute to an understanding of how algorithmic news is created and how algorithmic journalism technology impacts work.

}, url = {https://cpb-us-w2.wpmucdn.com/express.northeastern.edu/dist/d/53/files/2020/02/CJ_2020_paper_26.pdf}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/CJ_2020_paper_26.pdf}, author = {Ayse Dalgali and Kevin Crowston} } @proceedings {2020, title = {Factors Influencing Approval of Wikipedia Bots}, year = {2020}, address = {Wailea, HI}, abstract = {

Before a Wikipedia bot is allowed to edit, the operator of the bot must get approval. The Bot Approvals Group (BAG), a committee of Wikipedia bot developers, users and editors, discusses each bot request to reach consensus regarding approval or denial. We examine factors related to approval of a bot by analyzing 100 bots{\textquoteright} project pages. The results suggest that usefulness, value-based decision making and the bot{\textquoteright}s status (e.g., automatic or manual) are related to approval. This study may contribute to understanding decision making regarding the human-automation boundary and may lead to developing more efficient bots.

}, doi = {10.24251/HICSS.2020.018}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/HICSS_WikipediaPaper_3.9.new\%20kc\%20\%282\%29.pdf}, author = {Ayse Dalgali and Kevin Crowston} } @article {2020, title = {GitLab: Work where you want, when you want}, journal = {Journal of Organizational Design}, volume = {9}, year = {2020}, month = {2020/11/16}, pages = {23}, abstract = {

GitLab is a software company that works {\textquotedblleft}all remote{\textquotedblright} 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.

}, isbn = {2245-408X}, doi = {10.1186/s41469-020-00087-8}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/s41469-020-00087-8.pdf}, author = {Choudhury, Prithwiraj and Crowston, Kevin and Dahlander, Linus and Minervini, Marco S. and Raghuram, Sumita} } @proceedings {9999, title = {Sharing open deep learning models}, year = {2019}, abstract = {

We examine how and why trained deep learning (DL) models are shared, and by whom, and why some developers share their models while others do not. Prior research has examined sharing of data and software code, but DL models are a hybrid of the two. The results from a Qualtrics survey administered to GitHub users and academics who publish on DL show that a diverse population shares DL models, from students to computer/data scientists. We find that motivations for sharing include: increasing citation rates; contributing to the collaboration of developing new DL models; encouraging to reuse; establishing a good reputation; receiving feedback to improve the model; and personal enjoyment. Reasons for not sharing include: lack of time; thinking that their models would not be interesting for others; and not having permission for sharing. The study contributes to our understanding of motivations for participating in a novel form of peer-production.

}, doi = {10.24251/HICSS.2019.256}, url = { http://hdl.handle.net/10125/59650}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/Hicss\%20paper2018_DalgaliCrowston.pdf}, author = {Ayse Dalgali and Kevin Crowston} } @proceedings {2013, title = {Open Source Software Adoption: A Technological Innovation Perspective}, year = {2013}, month = {5/2013}, address = {Lyon, France}, abstract = {This research-in-progress aims to indentify the salient factors explaining adoption of open source software (OSS), as a technological innovation. The theoretical background of the paper is based on the technological innovation literature. We choose to focus on the open ERP case, as it is considered as a promising innovation for firms {\textendash} especially medium firms - but open ERP also faces numerous challenges. The paper provides a framework and a method for investigation that has to be implemented.}, url = {http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2244222}, author = {Kevin Crowston and Fran{\c c}ois Deltour and Nicolas Jullien} } @proceedings {Kwasnik:2006, title = {Challenges in creating a taxonomy for genres of digital documents}, year = {2006}, address = {Vienna, Austria}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/ISKO06abstract.pdf}, author = {Kwa{\'s}nik, Barbara H. and Chun, You-Lee and Kevin Crowston and D{\textquoteright}Ignazio, J. and Rubleske, Joe} } @article {Malone:1999, title = {Tools for inventing organizations: Toward a handbook of organizational processes}, journal = {Management Science}, volume = {45}, number = {3}, year = {1999}, note = {Reprinted in Malone, T. W., Crowston, K. \& Herman, G. (Eds.) Organizing Business Knowledge: The MIT Process Handbook. Cambridge, MA: MIT Press, 2003. Reprinted in Malone, T. W., Laubacher, R. \& Scott Morton, M. S. (Eds.). Inventing the Organizations of the 21st Century. Cambridge, MA: MIT Press, 2003.}, pages = {425{\textendash}443}, abstract = {This paper describes a novel theoretical and empirical approach to tasks such as business process redesign and knowledge management. The project involves collecting examples of how different organizations perform similar processes, and organizing these examples in an on-line "process handbook." The handbook is intended to help people: (1) redesign existing organizational processes, (2) invent new organizational processes (especially ones that take advantage of information technology), and (3) share ideas about organizational practices. A key element of the work is an approach to analyzing processes at various levels of abstraction, thus capturing both the details of specific processes as well as the "deep structure" of their similarities. This approach uses ideas from computer science about inheritance and from coordination theory about managing dependencies. A primary advantage of the approach is that it allows people to explicitly represent the similarities (and differences) among related processes and to easily find or generate sensible alternatives for how a given process could be performed. In addition to describing this new approach, the work reported here demonstrates the basic technical feasibility of these ideas and gives one example of their use in a field study.}, keywords = {Coordination, Handbook, Process}, doi = {10.1287/mnsc.45.3.425}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/ms99.pdf}, author = {Malone, Thomas W. and Kevin Crowston and Lee, Jintae and Pentland, Brian and Dellarocas, Chrysanthos and Wyner, George and Quimby, John and Osborn, Charley and Bernstein, Avi and Herman, George and Klein, Mark and O{\textquoteright}Donnell, Elissa} } @proceedings {Crowston:1996, title = {Market-enabling Internet agents}, year = {1996}, address = {Columbus, OH, December}, abstract = {The growth of the Internet offers a vision of ubiquitous electronic commerce. A particularly exciting feature of Internet commerce is the ability to automate the search for price or other product information across multiple suppliers by using an {\textquotedblleft}agent{\textquotedblright} to retrieve the relevant information. The use of such agents has the potential to dramatically reduce buyers{\textquoteright} search costs. We argue that such agents effectively transform a diverse set of offerings into an economically efficient market and that their use should therefore be analyzed in these terms. In this paper, we present a simple model of the competitive effects of agents used to support purchasing. The model suggests that agents can be successful for diversified goods but resisted for commodities and near-commodities. We illustrate our model by analyzing the situation of current electronic commerce ventures on the Internet. }, keywords = {Internet Agent}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/icis96.pdf}, author = {Kevin Crowston}, editor = {DeGross, Janice I. and Jarvenpaa, Sirkka and Srinivasan, Ananth} } @conference {1994, title = {Using a Process Handbook to design organizational processes}, booktitle = {Computational Organization Design: 1994 AAAI Spring Symposium }, year = {1994}, note = {Technical Report SS-94-07}, pages = {55-56}, publisher = {AAAI Press}, organization = {AAAI Press}, keywords = {Handbook, Process}, isbn = {9780929280806}, author = {Dellarocas, C and Lee, Jintae and Malone, Thomas W. and Kevin Crowston and Pentland, Brian}, editor = {Hulthage, Ingemar} }