Artificial intelligence methods to support people management in organisations

Tesis llevada a cabo para conseguir el grado de Doctor por la Universidad Autónoma de Barcelona--07-05-2018-Excelente cum laudem

Detalles Bibliográficos
Autor: Andrejczuk, Ewa
Tipo de recurso: tesis de maestría
Fecha de publicación:2018
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/197543
Acceso en línea:http://hdl.handle.net/10261/197543
Access Level:acceso abierto
Palabra clave:Approximate algorithms
Team composition
Peer assessment
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spelling Artificial intelligence methods to support people management in organisationsAndrejczuk, EwaApproximate algorithmsTeam compositionPeer assessmentTesis llevada a cabo para conseguir el grado de Doctor por la Universidad Autónoma de Barcelona--07-05-2018-Excelente cum laudemOrganisations have shifted from work arranged around individual jobs to teambased work structures. A new generation of solutions for organisations must give support to team management by encouraging team effectiveness and introducing automation. In this dissertation, we tackle several different problems that are connected to team management in organisations. In particular, we contribute by proposing a people management workflow that addresses the problems connected to team composition as well as problems of accurate employee evaluation and task performance evaluation. First, we review the literature on team composition and formation from both the organisational psychology and computer science perspectives and we explore the connection between individuals’ attributes and team performance as well as the cross fertilization opportunities between those fields. Second, we review the most prominent tools to measure individuals’ attributes, as these measures are necessary inputs for team composition processes. In particular, we describe the dominant approaches in Organisational Psychology, Industrial Psychology and Human Resources and summarise they main findings to measure individual personality and competences. Third, we use our findings to propose a model to predict team performance given a task and based on individuals’ attributes (i.e. competences, personality and gender). We define the Synergistic Team Composition Problem (STCP) as the problem of finding a team partition constrained by size so that each team, and the whole partition of employees into teams, is balanced in terms of individuals’ competences, personality and gender. We propose two different algorithms to solve this problem: an optimal algorithm called STCPSolver that is effective for small instances of the problem, and an approximate algorithm called SynTeam that provides high-quality, but not necessarily optimal solutions. We present empirical results that we obtained when analysing student performance. Our results show the benefits of a more informed team composition that exploits individuals’ competences, personalities and gender. Fourth, we devise an algorithm called Collaborative Judgment (CJ) to fairly evaluate individuals’ and teams’ outcomes once tasks are performed. In particular, we want to diminish the importance of biases in the evaluation process by allowing evaluators to assess their peers, namely other evalutors. Our empirical results show the benefits of more informed assessment aggregation method.Peer reviewedUniversidad Autónoma de BarcelonaCSIC - Instituto de Investigación en Inteligencia Artificial (IIIA)Sierra, CarlesRodríguez-Aguilar, Juan AntonioConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202018info:eu-repo/semantics/masterThesishttp://purl.org/coar/resource_type/c_bdcchttp://hdl.handle.net/10261/197543reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://hdl.handle.net/10803/565893Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1975432026-05-22T06:33:51Z
dc.title.none.fl_str_mv Artificial intelligence methods to support people management in organisations
title Artificial intelligence methods to support people management in organisations
spellingShingle Artificial intelligence methods to support people management in organisations
Andrejczuk, Ewa
Approximate algorithms
Team composition
Peer assessment
title_short Artificial intelligence methods to support people management in organisations
title_full Artificial intelligence methods to support people management in organisations
title_fullStr Artificial intelligence methods to support people management in organisations
title_full_unstemmed Artificial intelligence methods to support people management in organisations
title_sort Artificial intelligence methods to support people management in organisations
dc.creator.none.fl_str_mv Andrejczuk, Ewa
author Andrejczuk, Ewa
author_facet Andrejczuk, Ewa
author_role author
dc.contributor.none.fl_str_mv Sierra, Carles
Rodríguez-Aguilar, Juan Antonio
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Approximate algorithms
Team composition
Peer assessment
topic Approximate algorithms
Team composition
Peer assessment
description Tesis llevada a cabo para conseguir el grado de Doctor por la Universidad Autónoma de Barcelona--07-05-2018-Excelente cum laudem
publishDate 2018
dc.date.none.fl_str_mv 2018
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
http://purl.org/coar/resource_type/c_bdcc
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/197543
url http://hdl.handle.net/10261/197543
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://hdl.handle.net/10803/565893
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidad Autónoma de Barcelona
CSIC - Instituto de Investigación en Inteligencia Artificial (IIIA)
publisher.none.fl_str_mv Universidad Autónoma de Barcelona
CSIC - Instituto de Investigación en Inteligencia Artificial (IIIA)
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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