{"title":"Analysing social computing requirements with small group theory","authors":"A. Sutcliffe","doi":"10.1109/RESC.2011.6046715","DOIUrl":null,"url":null,"abstract":"Theories such as Small Group theory from social psychology and methods for socio-technical systems analysis which arose from research in HCI [4] can be usefully applied to RE practice. This paper has introduced the theory of Small Groups as Complex Systems [5] as a new resource for RE. The strength of SGCAS theory lies in its eclectic foundations in social psychology research, and its formalisation of sociological issues with a model theoretic approach. In contrast, Distributed Cognition [15] and Activity theory [16] both place more emphasis on human interaction with artefacts in the world. SGCAS theory can account for these issues in the task-agent-tool network, and provides the means of modelling the contribution of technology within a much richer social view of group interaction. Application of Small Group theory is limited to goal oriented collaborative systems (i.e. CSCW) with small groups, which may appear to limit its scale. However, most work oriented social systems, apart from the recent interest in ‘wisdom of crowds’ example, have a hierarchical structure inherent in the organisation of tasks. SGCAS can therefore scale to large scale collaborations in e-science since the requirements of teams within the larger scale collaboration will be similar. SGCAS theory provides a modelling framework within which RE social systems issues can be expressed. It could be used first as a diagnostic instrument to find potential problems in socio-technical systems and, secondly, as a source of social issues that can be combined with i* models to provide critical insight for improving RE.","PeriodicalId":332227,"journal":{"name":"2011 First International Workshop on Requirements Engineering for Social Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 First International Workshop on Requirements Engineering for Social Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RESC.2011.6046715","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Theories such as Small Group theory from social psychology and methods for socio-technical systems analysis which arose from research in HCI [4] can be usefully applied to RE practice. This paper has introduced the theory of Small Groups as Complex Systems [5] as a new resource for RE. The strength of SGCAS theory lies in its eclectic foundations in social psychology research, and its formalisation of sociological issues with a model theoretic approach. In contrast, Distributed Cognition [15] and Activity theory [16] both place more emphasis on human interaction with artefacts in the world. SGCAS theory can account for these issues in the task-agent-tool network, and provides the means of modelling the contribution of technology within a much richer social view of group interaction. Application of Small Group theory is limited to goal oriented collaborative systems (i.e. CSCW) with small groups, which may appear to limit its scale. However, most work oriented social systems, apart from the recent interest in ‘wisdom of crowds’ example, have a hierarchical structure inherent in the organisation of tasks. SGCAS can therefore scale to large scale collaborations in e-science since the requirements of teams within the larger scale collaboration will be similar. SGCAS theory provides a modelling framework within which RE social systems issues can be expressed. It could be used first as a diagnostic instrument to find potential problems in socio-technical systems and, secondly, as a source of social issues that can be combined with i* models to provide critical insight for improving RE.