The purpose of this study is to propose a measure for earned attention and a model and procedure for the maximization of earned attention by a company over a period of time.
Utility functions are used as the base of the earned attention measure. An evolutionary algorithm – a memetic algorithm – is applied to identify strategies that aim to maximize earned attention. Computational analysis is performed resorting to simulated data, and the memetic algorithm is assessed through the comparison with a standard steepest ascent heuristic.
The shape of the utility functions considered in the model has a huge impact on the characteristics of the best strategies, with actions focused on increasing a single variable being preferred in case of constant marginal utility, and more balanced strategies having a better performance in the case of decreasing marginal utility. The memetic algorithm is shown to have a much better performance that the steepest ascent procedure.
A new mathematical model for earned attention is proposed, and an approach that has few applications in business problems – a memetic algorithm – is tailored to the model and applied to identify solutions.
Godinho, P., Moutinho, L. and Pagani, M. (2017), "A memetic algorithm for maximizing earned attention in social media", Journal of Modelling in Management, Vol. 12 No. 3, pp. 364-385. https://doi.org/10.1108/JM2-10-2015-0078Download as .RIS
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