A Study of Teams: How It All Began The Apollo Syndrome Teams Containing Similar Personalities Identifying further Team Roles Team Leadership The Missing Team Roles Developing an inventory Unsuccessful teams Winning teams Ideal team size Features of good members of a team Teams in Public Affairs How Belbin reports developed Case Studies in Using Belbin

Management teams: Why they succeed or fail

Design and Marketing of New Products

http://robertfeldt.net/publications/lenberg_2015_behavioral_se.pdf

Behavioral software engineering

In line with increased attention on the application of cognitive approaches to industrial, work and organizational psychology, the last 40 years have witnessed a growing interest in application of the cognitive style construct to the field of business and management. The purpose of this paper is threefold. First, we wish to advance understanding of cognitive styles research by elucidating trends and perspectives related to business and management. Second, we identify gaps in the literature and promising areas of research that can be further developed. This is accomplished by means of a review of papers published between 1969 and 2009. Eight themes emerged from our analysis: (a) vocational and occupational issues; (b) national culture; (c) teamwork and interpersonal relationships; (d) learning; (e) decision making; (f) creativity, innovation and entrepreneurship; (g) sales and marketing; and (h) management information systems, information management and use. Third, we identify valid and reliable methods of assessment of cognitive style for use in business and management settings. Finally we draw a number of conclusions regarding the current state of cognitive styles research and promising directions for future research.

Role of Cognitive Styles in Business and Management: Reviewing 40 Years of Research

https://www.eng.uwo.ca/electrical/faculty/capretz_l/docs/publications/Shirley-CHB-SLR-v21.pdf

Forty years of research on personality in software engineering: A mapping study

The aim of the research described in this paper is to gain a qualitative understanding of how cohesiveness relates to personality type, performance and adherence to a particular software engineering methodology (XP). A variety of research methods were employed including ethnographic methods, questionnaires and interviews. An online personality test based on the Myers-Briggs Type Indicator (MBTI) was used to ascertain the personality types, and questionnaires were used throughout the project to measure levels of cohesiveness. Examples of how the teams worked together throughout the project are described, and whether and how this relates to the personality types of the individual members. The results indicate that certain teams were found to work consistently well over the project due to homogeneity in personality type and others were found to be very cohesive due to a mixture of types.

A study into the effects of personality type and methodology on cohesion in software engineering teams

This paper presents an approach in analyzing personality types, temperament and team diversity to determine software engineering (SE) teams performance. The benefits of understanding personality types and its relationships amongst team members are crucial for project success. Rough set analysis was used to analyze Myers-Briggs Type Indicator (MBTI) personality types, Keirsey temperament, team diversity, and team performance. The result shows positive relationships between these attributes.

Analyzing personality types to predict team performance

Problem statement: This study presents the initial findings on the impact of agile approach amongst Software Engineering (SE) teams in one computer center in Malaysia. This study revealed that agile approach able to receive positive feedbacks and increase positive affectivity amongst team members during software development projects. Approach: This study used action research approach. Data were collected from four Software Engineering (SE) teams who volunteered to participate in this study through a series of interviews, short-term observations and a questionnaire. Positive Affect scale from Positive Affect and Negative Affect Schedule (PANAS) questionnaire was used to measure the developers’ state of positive affect during two intervals. Results: These findings found that not agile-XP practices adopted by the developers. This is due to knowledge barriers and organizational culture. However, agile approach induced level of positive affectivity amongst the members during software development process. In addition, agile approach promotes the members to be more active in coordinating and collaborating amongst teams. Conclusion: The findings in this study give an insight towards applying a flexible agile approach to accommodate and suit organizational culture. Effective methodology and organizational culture are important factors that must be considered to produce innovative teams and quality software.

/pdf/the-impact-of-agile-approach-on-software-engineering-teams-4toloyeycn.pdf

The Impact of Agile Approach on Software Engineering Teams

This paper presents an application of rough sets in identifying effective personality-type composition in software engineering (SE) teams. Identifying effective personality composition in teams is important for determining software project success. It was shown that a balance of the personality types Sensing (S), Intuitive (N), Thinking (T) and Feeling (F) assisted teams in achieving higher software quality. In addition, Extroverted (E) members also had an impact on team performance. Even though the size of empirical data was too small, the rough-set technique allows the generation of significant personality-type composition rules to assist decision makers in forming effective teams. Future works will include more empirical data in order to develop predicting model of teams' performance based on personality types.

Identifying effective software engineering (SE) team personality types composition using rough set approach

This paper presents a rough sets approach in developing a team performance prediction model. To establish the prediction model, a dataset from an academic setting was obtained, consisting of four predictor variables: prior academic achievement, personality types, personality diversity, and software development methodology. In this study, four main steps in rough set, including discretisation, reduct generation rules, generation of decision rules, and evaluation were used to develop the prediction model. Two reduction algorithms; a genetic algorithm (GA) and a Johnson algorithm were used to obtain optimal classification accuracy. Results show that the Johnson algorithm outperformed the GA with 78.33% model prediction accuracy when using 10-fold cross validation. The result clearly shows that the rough sets is able to uncover complex factors in team dynamism, which revealed that the combination of the four predictor variables are important in developing the team performance prediction model. The model provides a practical contribution in predicting team performance.

Sharifah Lailee Syed-Abdullah

Papers

A study into the effects of personality type and methodology on cohesion in software engineering teams

Analyzing personality types to predict team performance

The Impact of Agile Approach on Software Engineering Teams

Identifying effective software engineering (SE) team personality types composition using rough set approach

Developing a Team Performance Prediction Model: A Rough Sets Approach