scholarly journals Testing an Integrated Team Effectiveness Framework in League of Legends

2019 ◽  
Vol 63 (1) ◽  
pp. 240-246
Author(s):  
Joshua A. Eaton ◽  
David J. Mendonça
Keyword(s):  
Path Analysis ◽  
Virtual Teams ◽  
Team Performance ◽  
Team Effectiveness ◽  
Secondary Data ◽  
Large Data ◽  
Data Sets ◽  
Government Data ◽  
Gaming Environment ◽  
League Of Legends

Objective: This paper investigates factors impacting team performance in the Multi-player Online Battle Arena gaming environment, League of Legends™, by testing an integrated Input Mediator-Outcome team effectiveness framework. Background: Secondary data and Naturally Occurring Data Sets (NODS) are data that have been collected from respondents without research interests in mind and can occur naturally in the environment. There are numerous sources of secondary data, including government data, financial databases, industry association groups, and Application Programming Interfaces, which this research utilizes to study the performance of teams. Methods: Path Analysis and Partial Least Squares Discriminant Analysis (PLS-DA) are analytical methods that are well suited for large data sets and sample sizes, confirmatory in nature, and can test a theoretical model. This research utilizes both in order to study factors impacting team performance. Results: A total of 5,927 matches from 742 teams are sampled and analyzed. Six team performance measures are used to discriminate between winning and losing teams, including role familiarity, team familiarity, team effectiveness, team efficiency, and the Kills, Deaths, Assist (KDA) ratio. Using path analysis and supervised PLS-DA, the models led to the successful prediction of 89.4% of the matches. The error rate for the PLS-DA model is 0.106 (Q2 = 0.523; R2 = 0.551). Conclusions: This work shows how objective, detailed data on teamwork may be used to provide insights into questions of the performance of teams. Additionally, the results demonstrate the value of using path analysis and PLS-DA to test an integrated framework. Application: This research highlights the value and feasibility of studying virtual teams for new insights into team performance.

Attack, Damage and Carry: Role Familiarity and Team Performance in League of Legends

2018 ◽  
Vol 62 (1) ◽  
pp. 130-134 ◽  
Author(s):  
Joshua A. Eaton ◽  
David J. Mendonça ◽  
Matthew-Donald D. Sangster
Keyword(s):  
Virtual Teams ◽  
Team Performance ◽  
Positive Impact ◽  
Social Constructs ◽  
Data Set ◽  
Team Members ◽  
Gaming Environment ◽  
And Performance ◽  
The Impact ◽  
League Of Legends

Objective: This research studies the impact of role familiarity on team performance by examining performance of the “Carry” role in the Multi-player Online Battle Arena gaming environment, League of Legends™. Background: Roles are typically defined as stable patterns of expectations, relationships, and behaviors. As social constructs, roles therefore include notions of status, relationships with additional social actors, and of defined sets of behaviors tied to the assigned role. We hypothesize that the importance of role familiarity in teams is mediated by the nature and extent of team members’ experience working together in defined roles. Methods: The data set used for this study is from League of Legends’ Application Program Interface and consists of ranked match play from 2011–2016. Results: ANOVA and visualization techniques are used to explore match-level data in order to address the proposed research questions. The proportion of time the same team member is assigned to the “Carry” role (role familiarity) has a direct and positive impact on team performance. Conclusions: This study shows how objective, detailed data on teamwork may be used to provide insights into questions of the composition and performance of teams. Additionally, the results illustrate the importance of role familiarity in the performance of teams. Application: This research highlights the value and feasibility of studying virtual teams for new insights into team performance.

The Impact of Trust on Virtual Team Effectiveness

2014 ◽  
Vol 4 (1) ◽  
pp. 11-28 ◽  
Author(s):  
Yasser A. El-Kassrawy
Keyword(s):  
Virtual Teams ◽  
Team Performance ◽  
Team Effectiveness ◽  
Virtual Team ◽  
Structural Equations ◽  
Team Members ◽  
Competitive Environments ◽  
Team Satisfaction ◽  
And Performance ◽  
The Impact

Given the important role of information technology, virtuality has become crucial issue in contemporary organizations. Virtual teams are comprised of members who are located in more than one physical location. They need to be effectively collaborating to harness their full performance capabilities in order to compete in the highly competitive environments. However, virtual team effectiveness is affected by determinants of trust which include three types; personality, cognitive and institutional-based trust. Therefore, this paper examines the impact of trust determinants on virtual team effectiveness represented in virtual team satisfaction and performance. Through a survey of 125 virtual team members who had experienced at least two years in this field, the results indicated that determinants of trust positively influence virtual team satisfaction and virtual team performance. The authors' structural equations modeling findings also support our hypothetical predictions that personality- based trust, cognitive- based trust and institutional- based trust have a dramatic impact on both of virtual team satisfaction and virtual team performance. Moreover, institutional- based trust is the uppermost driver of virtual team effectiveness. This study provides novel insights into virtual team behaviours, managerial and research implications for effective virtual team.

Carrying the Team: The Importance of One Player’s Survival for Team Success in League of Legends

2017 ◽  
Vol 61 (1) ◽  
pp. 272-276 ◽  
Author(s):  
Joshua A. Eaton ◽  
Matthew-Donald D. Sangster ◽  
Molly Renaud ◽  
David J. Mendonca ◽  
Wayne D. Gray
Keyword(s):  
Team Performance ◽  
Team Member ◽  
Online Game ◽  
Team Members ◽  
Team Success ◽  
The World ◽  
Gaming Environment ◽  
Visualization Techniques ◽  
Team Familiarity ◽  
League Of Legends

Objective: This research investigates the effect of “critical” team members and team familiarity on team performance in the Multi-player Online Battle Arena gaming environment, League of Legends™. Background: A critical team member is any member of a team whose presence (or absence) can have a dramatic impact on the team’s ability to reach their objective, while team familiarity can be viewed as the knowledge team members have about one another and the knowledge team members have about the tasks that must be accomplished. Methods: Data visualization techniques and logistic regression is used to explore team data collected from publicly accessible sources for the online game League of Legends, which is one of the most popular games in the world. Results: The proportion of time a team’s “Carry” is incapacitated (the “critical” team member) during a given match has a direct impact on how the team performs. Conclusions: The results show that critical team positions exist on teams, and can have a significant effect on achieving the team’s goals. In addition, there is a need for the development of tools, techniques and measures to bring “Big Data” to bear in the study of teamwork. Application: This research illustrates the feasibility of exploring online gaming data for new insights into team performance.

scholarly journals Understanding of Cultural Diversity and Team Performance: A Conceptual Framework

2020 ◽  
Vol 8 (6) ◽  
pp. 4566-4569
Keyword(s):  
Performance Analysis ◽  
Cultural Diversity ◽  
Conceptual Framework ◽  
Empirical Research ◽  
Team Performance ◽  
Cultural Difference ◽  
Team Effectiveness ◽  
Research Study ◽  
Secondary Data ◽  
Review Of Literature

This article is a conceptual framework which examines the effect of cultural difference on team effectiveness by assimilating literature, on the possible performance benefits of cultural diversity and possible problems of cultural diversity. The objective of the article is to provide practitioners and scholars, similar with a framework that will allow them to design cultural diversity initiatives based on a requirements assessment and to proceed with the empirical research. This research study is based on the review of literature on aspects such as cultural diversity and team performance. Analysis, conclusion and recommendations are based on the secondary data and their findings.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

An Input–Process–Output Model of Pilot Core Competencies

2017 ◽  
Vol 7 (2) ◽  
pp. 78-85 ◽  
Author(s):  
Heikki Mansikka ◽  
Don Harris ◽  
Kai Virtanen
Keyword(s):  
Path Analysis ◽  
Team Performance ◽  
Core Competencies ◽  
Core Competency ◽  
Principal Component ◽  
Analysis Model ◽  
Input Process ◽  
The Core ◽  
Performance Framework ◽  
Process Output

Abstract. The aim of this study was to investigate the relationship between the flight-related core competencies for professional airline pilots and to structuralize them as components in a team performance framework. To achieve this, the core competency scores from a total of 2,560 OPC (Operator Proficiency Check) missions were analyzed. A principal component analysis (PCA) of pilots’ performance scores across the different competencies was conducted. Four principal components were extracted and a path analysis model was constructed on the basis of these factors. The path analysis utilizing the core competencies extracted adopted an input–process–output’ (IPO) model of team performance related directly to the activities on the flight deck. The results of the PCA and the path analysis strongly supported the proposed IPO model.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

Sign in / Sign up

Export Citation Format

Share Document