Effects of Cohesion-Based Feedback on the Collaborations in Global Software Development Teams

Castro-Hernandez, Alberto; Swigger, Kathleen; Ponce-Flores, Mirna Patricia

doi:10.4108/icst.collaboratecom.2014.257332

Cited by 9 publications

(16 citation statements)

References 40 publications

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“…To maximize coverage of the literature, we take an interdisciplinary view of each process and emergent state. For example, cohesion (Salas, Grossman, Hughes, & Coultas, 2015) and language have been examined in many different disciplines and have been conceived of in terms of attraction in teamwork settings (Castro-Hernandez, Swigger, & Ponce-Flores, 2014) as well as in terms of attraction among group members (Manson, Bryant, Gervais, & Kline, 2013).…”

Section: Model Of Group Interactionmentioning

confidence: 99%

Language and Group Processes: An Integrative, Interdisciplinary Review

Swol

Kane

2018

Small Group Research

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This article reviews research that examines the use of language in small interacting groups and teams. We propose a model of group inputs (e.g., status), processes and emergent states (e.g., cohesion, influence, and innovation), and outputs (e.g., group effectiveness and member well-being) to help structure our review. The model is integrated with how language is used by groups to both reflect group inputs but also to examine how language interacts with inputs to affect group processes and create emergent states in groups, and then ultimately helps add value to the group with outputs (e.g., performance). Using cross-disciplinary research, our review finds that language is integral to how groups coordinate, interrelate, and adapt. For example, language convergence is related to increased group cohesion and group performance. Our model provides the theoretical scaffolding to consider language use in interacting small groups and suggests areas for future research.

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Section: Model Of Group Interactionmentioning

confidence: 99%

Language and Group Processes: An Integrative, Interdisciplinary Review

Swol

Kane

2018

Small Group Research

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“…These metrics could still be useful in settings where the size of the teams are large and everyone is an active participant such as in [11]. On the other hand, we found that Linguistic Style Matching and Information exchange had a statistically significant correlation with Task Cohesion.…”

Section: Resultsmentioning

confidence: 77%

“…This was achieved by focusing on specific factors that tend to affect team cohesion. These factors were derived from the research literature [19,20,46], and from smaller experiments performed by the author that looked at issues related to predicting the group constructs of Task cohesion [10,11].…”

Section: Methodsmentioning

confidence: 99%

“…This measure was introduced with the assumption that people view their group's cohesiveness as being a combination of their own participation as well as others. Thus, the Reply similarity measure was hypothesized as a variable that can predict Individual Task Cohesion, as cited in [11].…”

Section: Collaboration Measuresmentioning

confidence: 99%

“…However, the Work label was also found to be related to three LIWC categories: 1) Function words (FUNCTION), a category that includes words such as prepositions, articles, and common adverbs, which are often related to the idea of formal thinking [37]; 2) Work, a category that includes words such as accomplish, work, and success, which are often related to the idea of well-performed teams [11]; 3) Interrogatives (INTERROG), a category that includes words such as how, when, what, which are often used to represent the students' seeking input process.…”

Section: Class Instances Social 936mentioning

confidence: 99%

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Content and Temporal Analysis of Communications to Predict Task Cohesion in Software Development Global Teams

Castro-Hernandez

2016

2016 IEEE 11th International Conference on Global Software Engineering Workshops (ICGSEW)

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numbered references.Messages were collected from three software development projects that involved students from two different countries. The similarities and quantities of these interactions were computed and analyzed at individual and group levels. Results of interaction-based metrics showed that the collaboration variables most related to Task Cohesion were Linguistic Style Matching and Information Exchange. The study also found that Information Exchange rate and Reply rate have a significant and positive correlation to Task Cohesion, a factor used to describe participants' engagement in the global software development process. All these results suggest that metrics based on rate can be very useful for predicting cohesion in virtual groups. Similarly, content features based on communication categories were used to improve the identification of Task Cohesion levels. Also, at a group-level, all models were found correlated to Task Cohesion, specifically, Similarity+Rate, which suggests that models that include social and work communication categories are also good predictors of team cohesiveness. Finally, temporal interaction similarity measures were calculated to assess their prediction capabilities in a global setting. Results showed a significant negative correlation between the Pacing Rate and Task Cohesion, which suggests that frequent communications increases the cohesion between team Last but not least, I want to thank my family for always encouraging me and believing in me. This dissertation is also a result of their unconditional love and support.iii Another important team process that is affected by conditions within a remote collaboration setting is group cohesion. Previous research has found that communication technology (such as chat and email) often delays the development of the group cohesion construct [38]. As a result, global teams tend to have much lower group cohesion levels than co-located groups. One of the reasons why it is important to examine group cohesion levels within global virtual teams is that research has shown that this construct seems to affect how a team deals with different obstacles during the development of a project. In addition, the relation between group cohesion and other constructs (e.g. trust) has been shown to have an indirect relationship to team performance [21]. Using the above studies as a starting point, we studied the integration of metrics based on interaction type and amount, content features and temporal data to predict group cohesiveness.Our investigation used data collected in a collaborative setting in which participants from different countries worked together to complete a software development project.This study focused on the following aspects of the construct: first, it compared previous collaboration measures that had been used to analyze computer-based communications and determined which of these measures were most effective for predicting Task Cohesion; second, we developed a measure based on content features and determined if it could be used to im...

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