主讲人简介：

Ilan Oshri is the Director of the Centre of Digital Enterprise and the Disciplinary Area Lead of Behavioural IS at the University of Auckland Business School, New Zealand. Ilan’s research interests revolve around sourcing, work and innovation in business services. Ilan’s work was published in numerous journals including MIS Quarterly, Journal of Management of Information Systems, Journal of Association of Information Systems, Journal of Strategic Information Systems, Journal of Information Technology, The Wall Street Journal and others. Ilan has published 23 books and dozens of industry reports and teaching cases on global sourcing, digital transformation and emerging technologies. He is the co-founder of the Association for Information Systems （AIS） Special Interest Group on Advances in Sourcing, the Qualitative Paper Development Workshop, the Global Sourcing Workshop and the Research for Impact Workshop. Ilan is currently serving as Senior Editor for the Journal of Information Technology, Journal of Strategic Information Systems, MISQ Executive and on the editor board of Information Systems Research. Ilan has been teaching strategic management, international business and information systems core courses in New Zealand, USA, Netherlands, UK and China.

 

讲座简介：

Developing shared understanding between a client firm and a service provider has been a key requirement to meeting joint outsourcing engagement’s objectives. The assumption in the Information Systems （IS） outsourcing literature has persistently been that client and provider firms should continuously engage in the transfer of knowledge to ensure the co-creation of novel understandings between the parties throughout the engagement. Such shared understanding was constituted on the bi-directional exchange of domain knowledge, processes and procedures. In this paper, we challenge this assumption by examining the unique case of Machine Learning （ML） outsourcing services. Through a longitudinal study of an outsourcing engagement between a Bank and a ML service provider, we observed that shared understanding in ML outsourcing services manifested in two additional forms, namely as selective （i.e. the client firm partially understands the ML service） or inscrutable （i.e. the client firm does not understand the ‘inner workings’ of the ML service）. Yet, our study shows that such alternative forms of client–provider shared understanding that emerge in this ML outsourcing service do not challenge the alignment of objectives between the parties. We address our key research interest by theorising three shared understanding practices in which the client firm （i） blackboxed the technology, while the ML Provider （ii） transcended the explainability of the technology, and both parties （iii） pursued the demarcation of their expertise.  We conclude by offering implications for the extant literature and practice.