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Graceful Degradation and the Knowledge Worker

  • Dirk Nicolas Wagner
Chapter
Part of the Management – Culture – Interpretation book series (MCI)

Abstract

The term graceful degradation emerged in the information technology sector. It describes a functionality implemented by software programmers to help avoiding catastrophic failure when two machines interact. Inspired by Schirrmacher (2009), who describes the experience of permanent ungraceful degradation of brains in relation to existing information flood, degradation can also be observed to take place when machines interact with humans. This is of relevance to management thinkers and practitioners as it has adverse effects on individual knowledge workers and on organizations. To explore these effects, a conceptual framework is offered that helps to analyze different types of degradation. It is argued that when machines and knowledge workers interact, degradation can lead to a loss of productivity, a loss of competency, avoidance of responsibility and ultimately a loss of freedom. This occurs when the scarce resource of human attention becomes increasingly fragmented. It is concluded that a deliberate change of perspective can let managers pay attention to the management of attention. The expected dynamics suggest that knowledge workers will require machine assistance when it comes to attention management. As far as machine actors are concerned a property conceptualized here as new graceful degradation is expected to be required. Machine actors which gracefully degrade follow social rules which serve to protect the well-being of the knowledge worker.

Keywords

Knowledge Worker Information Overload Machine Actor Messaging System Crew Resource Management 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH 2017

Authors and Affiliations

  1. 1.Karlshochschule International UniversityKarlsruheGermany

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