Change in Managers’ Conceptions of Human Resources in Organisations : Outcomes of an Intervention

Managers’ conceptions of the importance of human resources are essential for creating ‘attractive workplaces’. This paper examines an intervention method aimed at creating insight among managers in small and medium-sized enterprises (SMEs) concerning the potential of human resources. The intervention method is called Focus Group Attractive Work (FGAW) and was conducted at eight enterprises in Sweden. Based on the analysis, it is concluded that the intervention method seems to be thought-provoking and to facilitate changes in managers’ conceptions of the importance of human resources, albeit to different degrees. 

Initiation into engineering : stability and change in gender orders

This article examines processes of doing gender during the initiation of students into engineering programs at university level in Sweden. The article draws on interviews with students, focusing on their understandings of gender. The aim is to explore difficulties with and challenges to traditional gender roles in an academic male dominated arena, by using theories of doing and undoing gender. The empirical material reveals the initiation period or ‘reception’ as a phenomenon both reinforcing and challenging traditional orders. The attempts to challenge norms meet resistance, revealing two paradoxes and one dilemma. In the first paradox the formal purpose of the reception (inclusion) is partly at odds with its informal consequence (exclusion of deviations). The second paradox concerns the contradictory effects of the reception. Even though the reception ensures participation of women, it reinforces existing hierarchies including gender inequality. This results in a dilemma, since in order to protect individual safety, there is a taboo on harassing women which then reproduces stable gender stereotypes. So while harassment taints the respect senior students must earn during the reception, the fact that female students exist in the engineering field challenges the established order and opens the way for change.

Machine Learning Algorithms in Heavy Process Manufacturing

In a global economy, manufacturers mainly compete with cost efficiency of production, as the price of raw materials are similar worldwide. Heavy industry has two big issues to deal with. On the one hand there is lots of data which needs to be analyzed in an effective manner, and on the other hand making big improvements via investments in cooperate structure or new machinery is neither economically nor physically viable. Machine learning offers a promising way for manufacturers to address both these problems as they are in an excellent position to employ learning techniques with their massive resource of historical production data. However, choosing modelling a strategy in this setting is far from trivial and this is the objective of this article. The article investigates characteristics of the most popular classifiers used in industry today. Support Vector Machines, Multilayer Perceptron, Decision Trees, Random Forests, and the meta-algorithms Bagging and Boosting are mainly investigated in this work. Lessons from real-world implementations of these learners are also provided together with future directions when different learners are expected to perform well. The importance of feature selection and relevant selection methods in an industrial setting are further investigated. Performance metrics have also been discussed for the sake of completion.