A Team Composition Approach For Social Crowdsourcing Communities
| dc.contributor.advisor | Barker, Ken E. | |
| dc.contributor.author | Zaamout, Khobaib | |
| dc.contributor.committeemember | Ruhe, Guenther | |
| dc.contributor.committeemember | Tang, Anthony Hoi Tin | |
| dc.date | 2020-11 | |
| dc.date.accessioned | 2020-09-28T13:56:37Z | |
| dc.date.available | 2020-09-28T13:56:37Z | |
| dc.date.issued | 2020-09-22 | |
| dc.description.abstract | This research takes place in an emerging paradigm of social computation that we name social crowdsourcing communities (SCCs). These are moderated online communities where members participate in collaborative activities (i.e. queries) designed to elicit their opinions concerning some topics, products, or services. This paradigm is distinct in that it combines the powers of crowdsourcing and social networking (SN) to allow for systematic querying of crowds and synthesizing response data (i.e. contributions) into coherent reports for decision-makers. SCCs consist of a beneficiary (i.e. the operators, the moderators, the analysts, and the organization that benefits from the reports), queries, a working crowd, and a platform where all activities occur. We show that it is possible to apply methods and techniques from existing fields to alleviate many of their challenges. One of these challenges is improving teamwork outcomes (i.e. contribution quality). Currently, SCC members, who are interested in a specific task, self-assemble into teams without considering any factors that may cause them to exhibit lower levels of productivity, participation, and contribution quality. The growth and query frequency restrictions imposed on these platforms by their operators to control operation costs further exacerbate this challenge. This thesis demonstrates how member behaviour can guide team formations and identifies specific behavioural characteristics related to improved team performance through an exploratory case study. It accomplishes this goal by capturing member behaviour in a model and using it to explore the compositions of existing teams. In doing so, this thesis identifies the specific compositions associated with increased team performance. The outcomes indicate the validity of this approach and provide a strong foundation for further investigation. | en_US |
| dc.identifier.citation | Zaamout, K. (2020). A Team Composition Approach For Social Crowdsourcing Communities (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/38256 | |
| dc.identifier.uri | http://hdl.handle.net/1880/112597 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Science | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject | Social Crowdsourcing Community | en_US |
| dc.subject | Crowdsourcing | en_US |
| dc.subject | Exploratory Case Study | en_US |
| dc.subject | Social Network Analysis | en_US |
| dc.subject | Team Composition | en_US |
| dc.subject | Team Formation | en_US |
| dc.subject | Data Science | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Statistical Analysis | en_US |
| dc.subject.classification | Computer Science | en_US |
| dc.title | A Team Composition Approach For Social Crowdsourcing Communities | en_US |
| dc.type | doctoral thesis | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Doctor of Philosophy (PhD) | en_US |
| ucalgary.item.requestcopy | true | en_US |