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
The Automation-by-Expertise-by-Training Interaction Why Automation-Related Accidents Continue to Occur in Sociotechnical Systems
Objective: I introduce the automation-by-expertise-by-training interaction in automated systems and discuss its influence on operator performance. Background: Transportation accidents that, across a 30-year interval demonstrated identical automation-related operator errors, suggest a need to reexamine traditional views of automation. Method: I review accident investigation reports, regulator studies, and literature on human computer interaction, expertise, and training and discuss how failing to attend to the interaction of automation, expertise level, and training has enabled operators to commit identical automation-related errors. Results: Automated systems continue to provide capabilities exceeding operators’ need for effective system operation and provide interfaces that can hinder, rather than enhance, operator automation-related situation awareness. Because of limitations in time and resources, training programs do not provide operators the expertise needed to effectively operate these automated systems, requiring them to obtain the expertise ad hoc during system operations. As a result, many do not acquire necessary automation-related system expertise. Conclusion: Integrating automation with expected operator expertise levels, and within training programs that provide operators the necessary automation expertise, can reduce opportunities for automation-related operator errors. Application: Research to address the automation-by-expertise-by-training interaction is needed. However, such research must meet challenges inherent to examining realistic sociotechnical system automation features with representative samples of operators, perhaps by using observational and ethnographic research. Research in this domain should improve the integration of design and training and, it is hoped, enhance operator performance.
Assessing Self-Organization in Agile Software Development Teams
Adarsh Kumar Kakar
Self-organization, based on the Socio-Technical Design (STS) work design principles, is considered a hallmark of agile software development (SD) teams and an antecedent of motivation and innovation at work. However, while self-organization is considered a salient success factor in agile SD, past research has shown that there are many hurdles in self-organizing. Yet, the actual level of self-organization practiced in agile teams has not been investigated. In this study we develop a measure to do so using the nine principles of STS work design by Cherns . We found that while the level of self-organization varies across agile projects, on each of the nine dimensions the level of self-organization in agile teams was found to be significantly higher than those using plan-driven methods. Furthermore, self-organization was found to positively affect the motivation and innovativeness of SD teams.
Sociotechnical regimes, technological innovation and corporate sustainability: from principles to action
Maria Fatima Ludovico de Almeida
Maria Angela Campelo de Melo
Increased environmental and social responsibility awareness, while producing unique opportunities for sustainability-oriented innovations, has generated important challenges for companies. The path to sustainability requires corporate strategies that guarantee profitability, managing simultaneously environmental and social responsibilities. An attempt is made to provide an understanding of sustainable development thinking in business, discussing how the combination of the transition management, adaptive planning and sociotechnical approaches can contribute towards an effective implementation of sustainability-oriented innovations in business context. The article proposes a conceptual model, which incorporates this contribution, developed through a four-year action-research project carried out within a large Brazilian energy company – Petrobras. The authors argue that the adoption of the proposed model by other large firms operating in different societal sectors might trigger organisational changes related to current corporate practices of technological innovation management.
Crisis informatics—New data for extraordinary times
Kenneth M. Anderson
Crisis informatics is a multidisciplinary field combining computing and social science knowledge of disasters; its central tenet is that people use personal information and communication technology to respond to disaster in creative ways to cope with uncertainty. We study and develop computational support for collection and sociobehavioral analysis of online participation (i.e., tweets and Facebook posts) to address challenges in disaster warning, response, and recovery. Because such data are rarely tidy, we offer lessons—learned the hard way, as we have made every mistake described below—with respect to the opportunities and limitations of social media research on crisis events. Focus on behaviors, not on fetishizing social media tools Focus on behaviors, not on fetishizing social media tools
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.
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