A Team-Formation Algorithm for Faultline Minimization

In recent years, the proliferation of online resumes and the need to evaluate large populations of candidates for on-site and virtual teams have led to a growing interest in automated team-formation. Given a large pool of candidates, the general problem requires the selection of a team of experts to complete a given task. Surprisingly, while ongoing research has studied numerous variations with different constraints, it has overlooked a factor with a well-documented impact on team cohesion and performance: team faultlines. Addressing this gap is challenging, as the available measures for faultlines in existing teams cannot be efficiently applied to faultline optimization. In this work, we meet this challenge with a new measure that can be efficiently used for both faultline measurement and minimization. We then use the measure to solve the problem of automatically partitioning a large population into low-faultline teams. By introducing faultlines to the team-formation literature, our work creates exciting opportunities for algorithmic work on faultline optimization, as well as on work that combines and studies the connection of faultlines with other influential team characteristics

Computational approaches for engineering effective teams

The performance of a team depends not only on the abilities of its individual members, but also on how these members interact with each other. Inspired by this premise and motivated by a large number of applications in educational, industrial and management settings, this thesis studies a family of problems, known as team-formation problems, that aim to engineer teams that are effective and successful. The major challenge in this family of problems is dealing with the complexity of the human team participants. Specifically, each individual has his own objectives, demands, and constraints that might be in contrast with the desired team objective. Furthermore, different collaboration models lead to different instances of team-formation problems. In this thesis, we introduce several such models and describe techniques and efficient algorithms for various instantiations of the team-formation problem. This thesis consists of two main parts. In the first part, we examine three distinct team-formation problems that are of significant interest in (i) educational settings, (ii) industrial organizations, and (iii) management settings respectively. What constitutes an effective team in each of the aforementioned settings is totally dependent on the objective of the team. For instance, the performance of a team (or a study group) in an educational setting can be measured as the amount of learning and collaboration that takes place inside the team. In industrial organizations, desirable teams are those that are cost-effective and highly profitable. Finally in management settings, an interesting body of research uncovers that teams with faultlines are prone to performance decrements. Thus, the challenge is to form teams that are free of faultlines, that is, to form teams that are robust and less likely to break due to disagreements. The first part of the thesis discusses approaches for formalizing these problems and presents efficient computational methods for solving them. In the second part of the thesis, we consider the problem of improving the functioning of existing teams. More precisely, we show how we can use models from social theory to capture the dynamics of the interactions between the team members. We further discuss how teams can be modified so that the interaction dynamics lead to desirable outcomes such as higher levels of agreement or lesser tension and conflict among the team members

The Impact of Board Diversity on Textual Social, Environmental Disclosures, and Corporate Performance

Drawing on the notion of faultlines – a hypothetical dividing line that splits a group into two or more subgroups based on the alignment of one or more individual attributes – this thesis proposes a new approach to the measurement and assessment of board diversity to understand how high(er) performing boards can be built i.e., the multi-dimensional diversity index (MDI). The proposed MDI captures the joint effect of differences in director attributes at four diversity levels for 26,743 directors, namely: (i) surface (or baseline); (ii) identity; (iii) demographic; and (iv) meso-level. The current study uses three-stage least squares (3SLS) with a panel of 3,357 FTSE All-Share index non-financial companies from 2005 to 2018. To this end, a key implication of this study – and by extension, the proposed MDI – is that it challenges the conventional notion that boards are improved ‘enough’ by focusing on the micro-dimension and increasing stand-alone diversity attributes, such as gender. Collectively, this study’s results suggest that a well-diversified board incentivises managers to disclose more information on social and environmental activities in contrast to firms with an extreme faultline score. The results show that highly effective boards with a moderate faultline score at meso-level diversity (e.g., identity, information, and non-demographic attributes) lead to better accounting profitability, corporate value, and market-based performance. Remarkably, the present study finds that nationality diversity per se positively impacts corporate performance; in contrast, the dominance of male directors hinders firm performance significantly

Supporting teachers in the design and implementation of group formation policies to carry out group learning activities in massive and variable scale on-line learning contexts

Los MOOC (Massive Open Online Courses, Cursos Abiertos Masivos en Línea), etiquetados como nuevo paradigma disruptivo en el entorno educativo, son criticados por un amplio sector de la comunidad educativa debido a sus altas tasas de abandono y a su baja calidad instruccional. La inclusión de pedagogías activas, tales como el aprendizaje colaborativo, en este tipo de cursos podría mejorar su calidad instruccional, además de aumentar la motivación e implicación de los alumnos. Sin embargo, la escala masiva y sus variaciones durante el curso, dificulta la introducción de dichas pedagogías y en especial la formación y mantenimiento de grupos de trabajo de alumnos. El apoyo a los profesores en las tareas de gestión de estos grupos, podría facilitar la adopción de diseños pedagógicos colaborativos. Para abordar esta meta y poder llevar a cabo el desarrollo de herramientas de apoyo a los profesores, es conveniente un conocimiento amplio y profundo del contexto y del problema a acometer, así como una visión holística del mismo. Por este motivo, este tesis propone como objetivo general, el dar apoyo a los profesores interesados en introducir actividades realizadas en grupo en este tipo de cursos, tanto en el diseño de las políticas de agrupación adecuadas para cada situación, como en la implementación de dichas políticas dentro de la plataforma educativa elegida. Para ello, se crea un marco conceptual que permita categorizar los factores relevantes a tener en cuenta para formar grupos de alumnos o equipos, en el contexto educativo MOOC, así como las principales características de este contexto que pueden influir en dichas agrupaciones. Tomando como base dicho marco, se desarrollan guías de diseño con recomendaciones y directrices que ayudan a los profesores a diseñar sus propias políticas de agrupación, así como herramientas informáticas de apoyo, que permitan implementar dichas políticas de agrupación en las diferentes plataformas educativas. A través de tres estudios en MOOCs reales y otras técnicas de investigación, tales como revisión de literatura y opinión de expertos, se han explorado propuestas de agrupación basadas en las analíticas de aprendizaje y las dinámicas de los alumnos monitorizadas durante el curso. Además, se ha generado un modelo para la creación de guías de diseño, y una arquitectura para el desarrollo de herramientas informáticas, independientes de la plataforma educativa elegida, que sirvan para implementar las agrupaciones diseñadas. Tomando como base estos modelos, se han creado pruebas de concepto que han permitido comprobar su viabilidad y su utilidad.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Doctorado en Informátic

Optimal locations of landfills and transfer stations in solid waste management

Cataloged from PDF version of article.In the recent years solid waste management has been given an increasing importance due to health factors and environmental concerns. Solid waste management refers to a complex task that covers the collection, transfer, treatment, recycling, resource recovery, and disposal of waste. In this thesis, we investigate the siting aspect of solid waste management for the siting of landfills and transfer stations. We first review the context of solid waste management and clarify the elements associated with it. We review the actual siting process applied by the authorities and compare it with the methods proposed by the researchers. We aim to examine how good the models used in optimization may be at approximating the actual siting process. For that purpose we formulate p-median models for several countries and compare the exisiting landfill locations with the cost-based optimal solutions. Another issue that we concentrate on is the siting of the transfer stations. We propose a new mixed integer programming model for the siting of the transfer stations and apply the proposed method for the city of Ankara.Beğen, Nilüfer NurM.S

Hazy Team Composition Processes: Shared Team Leadership, a Strategy to Team Excellence in Higher Education

Teamwork is an emergent property of efficacious organizations. Team-based and result-oriented organizational structures are gaining momentum, increasing 6% each year. Over 80% of organizations globally deploy teams by putting ordinary people to work together for extraordinary performance. The Defense Language Institute Foreign Language Center is a unique institute that teaches foreign languages in an immersive and team-based environment. This mixed-methods research study investigated (a) the teaching team composition processes, (b) the applicability of trust and diversity in team composition, and (c) the impact of shared team leadership in the Defense Language Institute Foreign Language Center. Data were collected from 82 faculty across eight undergraduate education schools of the Defense Language Institute Foreign Language Center (n = 66 quantitative; n = 16 qualitative). The analyzed quantitative data of Pearson correlations between the core themes of team composition processes showed that all items were positively related and significant at p = .01. Also, the amount of variance and diversity accounted for in the model (adj. R2 = -.031) was not significant F(8, 54) = .769, p = .631. The t-test analysis revealed no significance across demographic information of the respondents and diversity in the teams. The qualitative results found no standardized policy on team composition processes; teams were formed by the department chair(s), and the shared team leadership model only existed partially at the Defense Language Institute Foreign Language Center. As per the inputs, processes, and outputs model, prioritization of team composition processes will benefit the organization

An empirical investigation of the demographics of Top Management Team (TMT) and its influence in forecasting organizational outcome in international architecture, engineering and construction (AEC) Firms : a fuzzy set approach

Whereas Top Management Teams (TMTs) are selected to fit a firm’s strategy, prior studies have evidenced that TMTs have significant impact on firm performance. The challenge of the two-way causality has been reflected in previous findings being ambiguous, inconsistent and sometimes conflicted. Pursing the same line of research may lead to incomplete and even error-prone conclusion. In contrast, this research suggests that inconsistency of findings among TMT demographics shown in prior work may point the possibility of studying the black-box nature of such relationships, and provide a tool to future forecast the organization outcome. More specifically, a multi-input (TMT demographics) multi-output (organization outcome) structure was used in this research to explore the future predictability power of TMT demographics for international Architects, Engineers and Construction firms (AEC firms). In order to build a reliable forecasting model, those contradictions were avoided by the utilization of artificial intelligence methods by training, testing and producing results without any prior assumptions or known structures. In particular, the Adaptive Neural Fuzzy Inference System (ANFIS) have been employed as a basis for constructing a set of fuzzy “if– then” rules with pre-tested input–output pairs. Three different forecasting strategies were constructed, the findings have demonstrated the learning and potential of the ANFIS model (time series based) in forecasting organization outcome, but at the same time, suggest that distinction should be established among different constructs of TMT demographics and outcome constructs. The results demonstrated that job-related demographics (i.e., TMT Educational Diversity, TMT Functional Diversity and TMT Tenure) could provide a satisfactory forecasting accuracy for the short-span (Liquidity) and medium-span (Cash Flow Stability and Capital Structure) outcome constructs. The future predictability power of other non-job demographics could not be evidenced in this research. Additionally, outcome constructs with dynamic nature could not be forecasted. Lastly, future research opportunities have been suggested for researchers. Most importantly, it includes the need to re-define diversity in the context of TMT composition (having different meaning as in: Variety, Separation and Disparity). Other methodological future opportunities are also suggested at the end of this study