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What prevents machine learning from transforming industries?

PublicationBook chapter

Abstract

The industrial utilization of machine learning (ML) technology is still in its infancy. This chapter provides empirical insights on how ML has been deployed in three firms and which forces are at work in this transformation. It is clear that two complementary advancements are needed to make ML generally useful: while ML technology thrives on access to big and varied datasets, the first advance is a reduction in the laborious work of manually cleaning, sorting and labelling the data, which defines how knowledge creation, technology and organization are interrelated. The second advance is to find sensible collaborative modes of data access and sharing, which challenges the very boundaries and interdependence of firms since the value of data for training ML algorithms depends on access to others’ data.

Long, V., & Grafström, J. (2021). What prevents machine learning from transforming industries?. In Technological Change and Industrial Transformation (pp. 125-140). Routledge.

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What prevents machine learning from transforming industries?
BokkapitelPublication
Long, V., & Grafström, J.
Publication year

2021

Abstract

The industrial utilization of machine learning (ML) technology is still in its infancy. This chapter provides empirical insights on how ML has been deployed in three firms and which forces are at work in this transformation. It is clear that two complementary advancements are needed to make ML generally useful: while ML technology thrives on access to big and varied datasets, the first advance is a reduction in the laborious work of manually cleaning, sorting and labelling the data, which defines how knowledge creation, technology and organization are interrelated. The second advance is to find sensible collaborative modes of data access and sharing, which challenges the very boundaries and interdependence of firms since the value of data for training ML algorithms depends on access to others’ data.

Long, V., & Grafström, J. (2021). What prevents machine learning from transforming industries?. In Technological Change and Industrial Transformation (pp. 125-140). Routledge.

Exploring regional differences in the regional capacity to absorb displacements
Book chapterPublication
Nyström, K., & Viklund Ros, I.
Publication year

2017

Abstract

Extract: Every year there is substantial turbulence in economies with respect to establishing new firms and business closures. Job displacement, i.e. an involuntary loss of jobs due to economic downturns or structural changes affects millions of workers each year. A recent cross-country comparison by the Organisation for Economic Co-operation and Development (OECD) (2013) reveals that displacements affect 2–7 percent of employees every year. In the Swedish case, an average displacement rate of about 2 percent is reported for the time period 2000–2008. According to Tillvaxtanalys (2009)1 more than 100 000 Swedish employees lose their jobs annually due to business closures.2 Through the process of creative destruction, in which old and obsolete firms exit due to the entry of new and more productive firms, the resources used in the exiting firms are reallocated and possibly more efficiently used in the new firms. However, in some cases displaced workers are not able to find a new job, especially if, for example, the employee’s competences do not match the current demands in the labor market. Furthermore, the possibilities to find a new job after a closure may vary substantially depending on the regional conditions in the labor market. It may, for instance, be more difficult to find a new job after a business closure if the unemployment rate in the region is already high or if the displacement is connected to the closure of a locally dominant firm.

Making Sense of Local Customer Relationships in Cross-border Acquisitions
Book chapterPublication
Öberg, C.
Publication year

2017

Abstract

Excerpt: This chapter deals with sensemaking in the context of customer relationships in cross-border acquisitions. Its specific focus is on the local customer relationships of acquired parties, highlighting the differences in sensemaking that exist between local customers, local representatives of the acquired parties and strategic managers of the acquirer. Sensemaking is concerned with how individuals understand a situation (Weick 1987), which in the context of this chapter refers to both the acquisition and subsequent integration.

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