This website is a hub for sociotechnical systems research and teaching. The site is sponsored by the Consortium for the Science of Sociotechnical Systems, and is a home for community building, resource-sharing, and expanding the breadth, depth, impact and visibility of sociotechnical systems scholarship.
Latest sociotechnical systems research
A scholarly divide: Social media, Big Data, and unattainable scholarship
Erik P. Bucy
Recent decades have witnessed an increased growth in data generated by information, communication, and technological systems, giving birth to the ‘Big Data’ paradigm. Despite the profusion of raw data being captured by social media platforms, Big Data require specialized skills to parse and analyze — and even with the requisite skills, social media data are not readily available to download. Thus, the Big Data paradigm has not produced a coincidental explosion of research opportunities for the typical scholar. The promising world of unprecedented precision and predictive accuracy that Big Data conjure remains out of reach for most communication and technology researchers, a problem that traditional platforms, namely mass media, did not present. In this paper, we evaluate the system architecture that supports the storage and retrieval of big social data, distinguishing between overt and covert data types, and how both the cost and control of social media data limit opportunities for research. Ultimately, we illuminate a curious but growing ‘scholarly divide’ between researchers with the technical know-how, funding, or institutional connections to extract big social data and the mass of researchers who merely hear big social data invoked as the latest, exciting trend in unattainable scholarship.
Research synthesis: Social media analyses for social measurement
Michael F. Schober
Frederick G. Conrad
Demonstrations that analyses of social media content can align with measurement from sample surveys have raised the question of whether survey research can be supplemented or even replaced with less costly and burdensome data mining of already-existing or “found” social media content. But just how trustworthy such measurement can be—say, to replace official statistics—is unknown. Survey researchers and data scientists approach key questions from starting assumptions and analytic traditions that differ on, for example, the need for representative samples drawn from frames that fully cover the population. New conversations between these scholarly communities are needed to understand the potential points of alignment and non-alignment. Across these approaches, there are major differences in (a) how participants (survey respondents and social media posters) understand the activity they are engaged in; (b) the nature of the data produced by survey responses and social media posts, and the inferences that are legitimate given the data; and (c) practical and ethical considerations surrounding the use of the data. Estimates are likely to align to differing degrees depending on the research topic and the populations under consideration, the particular features of the surveys and social media sites involved, and the analytic techniques for extracting opinions and experiences from social media. Traditional population coverage may not be required for social media content to effectively predict social phenomena to the extent that social media content distills or summarizes broader conversations that are also measured by surveys.
Creating knowledge within a team: a socio-technical interaction perspective
One-Ki (Daniel) Lee
Creating knowledge within a team for developing new products and services is considered a primary means for improving organizational performance. Drawing upon the socio-technical perspective, we investigate the blended effects of social (learning culture, teamwork quality, and knowledge complexity) and technical (IT support) factors on team-level knowledge creation and team performance. We propose a model that features synergetic interactions between social and technical factors in this knowledge creation process. The model was tested by utilizing data from a field survey of industry managers. The results show significant interactions between social and technical factors, which influence team-level knowledge creation and, in turn, team performance. Our findings can be used to develop socio-technical initiatives to enhance the process of creating team-level knowledge within firms.
DesignX: Complex Sociotechnical Systems
Donald A. Norman
Pieter Jan Stappers
Formal Modelling and Analysis of Socio-Technical Systems
Christian W. Probst
René Rydhof Hansen
Christian W. Probst
Attacks on systems and organisations increasingly exploit human actors, for example through social engineering. This non-technical aspect of attacks complicates their formal treatment and automatic identification. Formalisation of human behaviour is difficult at best, and attacks on socio-technical systems are still mostly identified through brainstorming of experts. In this work we discuss several approaches to formalising socio-technical systems and their analysis. Starting from a flow logic-based analysis of the insider threat, we discuss how to include the socio aspects explicitly, and show a formalisation that proves properties of this formalisation. On the formal side, our work closes the gap between formal and informal approaches to socio-technical systems. On the informal side, we show how to steal a birthday cake from a bakery by social engineering.
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