Operational autonomy index, 2017

Abstract Research partnerships between university researchers and industry partners are becoming increasingly prevalent. For university researchers, maintaining autonomy is crucial. We explore how researchers strategically manage autonomy in collaborative research partnerships, using a framework to distinguish strategically planned and opportunity-driven behaviour in the process of selecting partners and executing research in partnerships. We then focus on the management of autonomy in setting research directions and managing the research process. We draw on insights from 14 management scholars engaged in collaborative Ph.D. research projects. Based on our analysis, we show that researcher autonomy has two facets: operational and scientific. Researchers are willing to compromise their operational autonomy as a price for industry collaboration. They have a strong need for scientific autonomy when deciding on research direction and research execution. Although they need funding, entering a specific relationship with industry and accepting restrictions on their operational autonomy is a choice. We conclude that researchers’ orientations towards practice and theory affects their choices in partnerships as well as modes of governance.

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“The Master of the House”—Agencies in Municipal Service Provision: Balancing Autonomy and Accountability

Administration & Society ◽

10.1177/0095399714555749 ◽

2014 ◽

Vol 49 (5) ◽

pp. 730-752 ◽

Cited By ~ 2

Author(s):

Ole Johan Andersen ◽

Harald Torsteinsen

Keyword(s):

Strategic Management ◽

Service Provision ◽

Continuous Process ◽

Years Of Experience ◽

Agency Model ◽

Municipal Service ◽

Operational Autonomy

The article raises the question of to what extent municipalities adopting reforms of decentralization are able to find a stable balance between strategic management and operational autonomy. We performed a case study in a Norwegian municipality with more than 10 years of experience in practicing the agency model characterized by a radical disaggregation and autonomization of functions. Our findings suggest that finding a stable balance seems hard to obtain. Instead, there is a continuous process of “negotiation” going on between the two levels. The risk of sliding back to hierarchy and central rule seems to be more or less permanent.

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Enabling Operational Autonomy for UAVs with Robustness

10.2514/6.2013-4652 ◽

2013 ◽

Cited By ~ 1

Author(s):

Sunghun Jung ◽

Kartik B. Ariyur

Keyword(s):

Operational Autonomy

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Computing Machinery, Surprise and Originality

Philosophy & Technology ◽

10.1007/s13347-021-00453-8 ◽

2021 ◽

Author(s):

Sylvie Delacroix

Keyword(s):

Limited Ability ◽

Learning Machines ◽

Computing Machinery ◽

Surprise Question ◽

Future Learning ◽

Digital Computers ◽

Operational Autonomy

AbstractLady Lovelace’s notes on Babbage’s Analytical Engine (1843) never refer to the concept of surprise. Having some pretension to ‘originate’ something—unlike the Analytical Engine—is neither necessary nor sufficient to being able to surprise someone. Turing nevertheless translates Lovelace’s ‘this machine is incapable of originating something’ in terms of a hypothetical ‘computers cannot take us by surprise’ objection to the idea that machines may be deemed capable of thinking. To understand the contemporary significance of what is missed in Turing’s ‘surprise’ translation of Lovelace’s insight, one needs to distinguish between trivial surprises (which stem from our limited ability to store data and process it) and those events, propositions or encounters that lead us to question our understanding of ourselves or what surrounds us. Only some of these non-trivial surprises are the product of originality endeavours. Not only is it uncommon for surprises to track such endeavours, the type of autonomy that would be required on the part of ‘digital computers’ for originality and surprise to intersect in that way goes far beyond the operational autonomy that can be achieved by ‘learning machines’. This paper argues that a salient translation of Lovelace’s originality insight—for contemporary and future ‘learning machines’—is an upside-down version of Turing’s surprise question: can computers be surprised by us in a non-trivial, ‘co-produced’ way?

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