Computing Machinery, Surprise and Originality

Lady 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?

Managing autonomy in university–industry research: a case of collaborative Ph.D. projects in the Netherlands

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.

How does subsidiary autonomy influence performance? The moderating role of uncertainty

Purpose The purpose of this paper is to explore the influence of subsidiary autonomy on subsidiary performance under uncertainty. Based on previous studies, the authors classify subsidiary autonomy into two categories, namely, strategic autonomy and operational autonomy, and investigate the relationships between these two categories of subsidiary autonomy and subsidiary performance under uncertainty. Design/methodology/approach The sample includes the subsidiaries listed on the Shanghai and Shenzhen Stock Exchanges in China from 2012 to 2015. Ordinary least squares are used to examine the hypotheses. Findings The results indicate that strategic autonomy is negatively related to subsidiary performance, whereas operational autonomy is positively associated with subsidiary performance. Moreover, uncertainty weakens the negative strategic autonomy-subsidiary performance linkage. Originality/value The findings of this study indicate that two categories of subsidiary autonomy (strategic autonomy, operational autonomy) have different effects on subsidiary performance. Moreover, uncertainty moderates the above relationships. This study explores the relationship between subsidiary autonomy and subsidiary performance and provides a useful guidance for the selection of subsidiaries’ management modes.

Learning from Singapore’s effective anti-corruption strategy

Purpose The purpose of this paper is to explain why Singapore has succeeded in curbing corruption and to recommend three measures for enhancing South Korea’s anti-corruption strategy. Design/methodology/approach The paper compares the contextual differences between Singapore and South Korea before analysing Singapore’s effective anti-corruption strategy and identifying the weaknesses of South Korea’s anti-corruption strategy. Findings Singapore’s success in minimising corruption is the result of its government’s strong political will and the adequate budget, personnel and operational autonomy given to the Corrupt Practices Investigation Bureau to enable it to enforce the anti-corruption laws impartially. To improve South Korea’s anti-corruption strategy, the Korea Anti-Corruption Agency should be established and adequately staffed and funded to investigate corruption cases. Those found guilty of corruption offences should be punished according to the law, without suspending their jail sentences or being pardoned by the president. Finally, the existing public outreach anti-corruption programmes should be evaluated to identify their weaknesses and improve their effectiveness. Originality/value This paper recommends three measures for South Korean policy-makers to improve their anti-corruption strategy by learning from Singapore’s success.

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

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.