@proceedings {9999, title = {Impacts of the Use of Machine Learning on Work Design}, year = {2020}, month = {11/2020}, publisher = {ACM}, address = {Virtual Event, NSW, Australia}, abstract = {

The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) to automate information tasks, we draw on a simple framework for identifying the impacts of an automated system on a task that suggests 3 patterns for the use of ML{\textemdash}decision support, blended decision making and complete automation. In this paper, we extend this framework by considering how automation of one task might have implications for interdependent tasks and how automation applies to coordination mechanisms.

}, keywords = {artificial intelligence, automation, Coordination, machine learning, work design}, isbn = {978-1-4503-8054-6/20/11}, doi = {10.1145/3406499.3415070}, attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/Impacts_of_ML_for_HAI_2020.pdf}, author = {Kevin Crowston and Bolici, Francesco} } @proceedings {9999, title = {Helping data science students develop task modularity}, year = {2019}, abstract = {

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.

}, 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} } @proceedings {9999, title = {Impacts of machine learning on work}, year = {2019}, address = {Wailea, HI}, abstract = {

The increased pervasiveness of technological advancements in automation makes it urgent to address the question of how work is changing in response. Focusing on applications of machine learning (ML) that automate information tasks, we present a simple framework for identifying the impacts of an automated system on a task. From an analysis of popular press articles about ML, we develop 3 patterns for the use of ML--decision support, blended decision making and complete automation--with implications for the kinds of tasks and systems. We further consider how automation of one task might have implications for other interdependent tasks. Our main conclusion is that designers have a range of options for systems and that automation of tasks is not the same as automation of work.

}, keywords = {artificial intelligence, automation, machine learning, work design}, doi = {10.24251/HICSS.2019.719}, url = { http://hdl.handle.net/10125/60031}, attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/Impacts_of_machine_learning_on_work__revision_.pdf}, author = {Kevin Crowston and Bolici, Francesco} } @conference {2011, title = {Gaming for (citizen) science: Exploring motivation and data quality in the context of crowdsourced science through the design and evaluation of a social-computational system}, booktitle = {{\textquotedblleft}Computing for Citizen Science{\textquotedblright} workshop at the IEEE eScience Conference}, year = {2011}, month = {12/2011}, address = {Stockholm, Sweden}, abstract = {In this paper, an ongoing design research project is described. Citizen Sort, currently under development, is a web-based social-computational system designed to support a citizen science task, the taxonomic classification of various insect, animal, and plant species. In addition to supporting this natural science objective, the Citizen Sort platform will also support information science research goals on the nature of motivation for social-computation and citizen science. In particular, this research program addresses the use of games to motivate participation in social-computational citizen science, and explores the effects of system design on motivation and data quality. A design science approach, where IT artifacts are developed to solve problems and answer research questions is described. Research questions, progress on Citizen Sort planning and implementation, and key challenges are discussed.}, keywords = {Citizen Science, data quality, Design, Design Science, Games, Gaming, Motivation, Participation, Social Computational Systems}, url = {http://itee.uq.edu.au/~eresearch/workshops/compcitsci2011/index.html}, attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/gamingforcitizenscience_ver6.pdf}, author = {Nathan Prestopnik and Kevin Crowston} } @proceedings {, title = {The role of mental models in FLOSS development work practices}, year = {2006}, pages = {91-97}, address = {Lake Como, Italy, 8{\textendash}9 June}, abstract = {Shared understandings are important for software development as they guide to effective individual contributions to, and coordination of, the software development process. In this paper, we present the theoretical background and research design for a proposed study on shared mental models within Free/Libre Open Source Software (FLOSS) development teams. In particular, we plan to perform case studies on several projects and to use cognitive maps analysis to represent and compare the mental models of the involved members so as to gauge the degree of common knowledge and the development of a collective mind as well as to better understand the reasons that underlie team members actions and the way common mental models, if any, arise.}, keywords = {FLOSS, Mental Model}, attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/oss2006crowstonscozzi.pdf}, author = {Kevin Crowston and Scozzi, Barbara} } @article {, title = {The effects of market-enabling Internet agents on competition and prices}, journal = {Journal of Electronic Commerce Research}, volume = {2}, number = {1}, year = {2001}, pages = {1-22}, abstract = {The Internet offers a vision of ubiquitous electronic commerce. A particularly useful feature is the ability to automate the search for price or other information across multiple vendors by using an {\textquotedblleft}agent{\textquotedblright} to retrieve relevant information. The use of agents has the potential to dramatically reduce buyers{\textquoteright} search costs. We develop a framework that suggests that vendors who sell products with many differentiating factors beyond price will tend to accept agents, while vendors of commodities or branded goods will tend to resist them unless they have lower costs than their competitors. Empirically, we found that agents seem to be accepted for differentiated goods, but resisted for more commoditized goods, though not universally. An analysis of prices from one agent shows that 1) a small number of vendors tended to have the lowest prices and 2) while divergence in pricing remains, price dispersion declined over the period studied.}, keywords = {Electronic Commerce, Internet Agent, Market-enabling}, attachments = {https://crowston.syr.edu./sites/crowston.syr.edu/files/joecr01.pdf}, author = {Kevin Crowston and MacInnes, Ian} }