In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making.
Recent developments in the gig economy triggered labour law. Platforms change the relationship between customers and workers causing over-subordination of workers. The over-subordination is caused by customers and the surveys they complete to evaluate the worker. The influence of customers over workers can also be seen in a more traditional setting. However, customers are not always king. It is suggested in this article that surveys can be used as an instrument to build decent labour relationships. Installing the measurement of needs measures the impact of customers on workers. By installing a continuous measure of the needs, we have at our disposal an instrument to fulfil labour law’s wellbeing function. Labour legislation could oblige employers to integrate need satisfaction into their customer surveys and their workers surveys. By doing so, technology allows us to make sure that platform work or any kind of work where workers meet high customer demands, become ‘Innovative forms of work that ensure quality working conditions’, as requested by the European Social Pillar.
In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making.
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