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
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.
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.
Pilot error versus sociotechnical systems failure: a distributed situation awareness analysis of Air France 447
Paul M. Salmon
Guy H. Walker
Neville A. Stanton
The Air France 447 crash occurred in 2009 when an Airbus A330 stalled and fell into the Atlantic Ocean, killing all on board. Following a major investigation, it was concluded that the incident resulted from a series of events that began when the autopilot disconnected after the aircraft's Pitot tubes froze in an adverse weather system. The findings place scrutiny on the aircrew's subsequent lack of awareness of what was going on and of what procedure was required, and their failure to control the aircraft. This article argues that this is inappropriate, instead offering a systems level view that can be used to demonstrate how systems, not individuals, lose situation awareness. This is demonstrated via a distributed situation awareness-based description of the events preceding the crash. The findings demonstrate that it was the sociotechnical system comprising aircrew, cockpit and aeroplane systems that lost situation awareness, rather than the aircrew alone.
Understanding Human-Machine Networks: A Cross-Disciplinary Survey
Eric T. Meyer
J. Brian Pickering
In the current hyper-connected era, modern Information and Communication Technology systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such human-machine networks (HMNs) are embedded in the daily lives of people, both or personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, nor following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.
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