Augmenting Transactive Memory Systems in Virtual Teams by means of Natural Language Processing and Machine Learning

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Schildt, Henri Armel, Jamal 2017-09-04T12:57:36Z 2017-09-04T12:57:36Z 2017-08-23
dc.description.abstract A Transactive Memory System (TMS) is a mechanism that captures the ability of teams to encode, store and retrieve knowledge collectively. TMS, thus, helps in locating “who knows what”. Such knowledge enables team members in an organisation to solve problems requiring knowledge beyond their own expertise, and has thus been suggested as one of the microfoundations of dynamic capabilities – the ability of organisations to renew themselves. TMS has been shown to be valuable for efforts to integrate and renovate knowledge assets of the organisation. However, prior research on TMS has focused mainly on face-to-face teams with only few studies considering the more difficult case of distributed work arrangements. In this research, I will expand on this proposition and present a computational framework that supports TMS in virtual teams. The objective of the research was to broadly examine the ways in which machine learning algorithms and natural language processing techniques could be employed to provide support to TMS in virtual teams. Specifically, this research builds and evaluates a computational framework that pushes the boundaries of knowledge on distributed work arrangements through the lens of TMS. The research methodology followed the design science research. The validation of the computational framework has been done using data mined from archived mailing lists of a real Free Open Source Software development virtual team. In order to identify who knows what in the studied virtual team, I used mined data from experts’ conversations and survey data. Based on these foundations, I built a computational framework that involves two main components: The first component handles the mining of raw textual data and the second handles the classification of this data into broad areas of expertise. My findings highlight the impediments to TMS in virtual teams and prove the usefulness of machine learning techniques and natural language processing in identifying expertise. Also, these findings suggest that it is possible and beneficial to support TMS through algorithmic means. From a theoretical point of view, this research contributes to the TMS research with a novel framework for augmenting TMS in distributed work arrangements. These findings are generalisable to a similar type of virtual teams. Although, only a limited number of skills were considered, the developed computational framework can be improved and extended to include a greater range of skills and other types of communities. en
dc.format.extent 68+1
dc.language.iso en en
dc.title Augmenting Transactive Memory Systems in Virtual Teams by means of Natural Language Processing and Machine Learning en
dc.type G2 Pro gradu, diplomityö fi Perustieteiden korkeakoulu fi
dc.subject.keyword transactive memory systems en
dc.subject.keyword talent analytics en
dc.subject.keyword machine learning en
dc.subject.keyword natural language processing en
dc.subject.keyword distributed work arrangements en
dc.subject.keyword Python en
dc.identifier.urn URN:NBN:fi:aalto-201709046865
dc.programme.major Strategy fi
dc.programme.mcode SCI3051 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Schildt, Henri
dc.programme Master’s Programme in Industrial Engineering and Management fi
local.aalto.electroniconly yes
local.aalto.openaccess no

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