Vera Miguéis

Abstract
In celebration of the 10th anniversary of the International Conference on Exploring Service Science (IESS), this paper takes a historical look at the papers that have been published in the IESS proceedings. The analysis is focused on the development and evolution of the IESS community and of the main research topics covered by the published papers over time. The IESS community is portrayed in terms of authors, their affiliations and co-authoring network, while the topics are analyzed according to the papers’ keywords. Moreover, this paper analyzes the impact of the papers published in this decade, in terms of citations. These results are then discussed in light of the observed trends and of the evolution of the service science field, to guide the future development of the IESS conference and of research on service science. © Springer Nature Switzerland AG 2020.

Abstract
This study proposes two predictive models of classification that allow to identify, at the end of the 1st and 2nd semesters, the undergraduate students of a higher education institution more prone to academic dropout. The proposed methodology, which combines 3 popular data mining algorithms, such as random forest, support vector machines and artificial neural networks, in addition to contributing to predictive performance, allows to identify the main factors behind academic dropout. The empirical results show that it is possible to reduce to about 1/4 the 4 tens potential predictors of dropout, and show that there are essentially two predictors, concerning student’s curriculum context, that explain this propensity. This knowledge is useful for decision-makers to adopt the most appropriate strategic measures and decisions in order to reduce student dropout rates.

Abstract

Abstract
The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure. © 2019, Springer Nature Switzerland AG.

Abstract
Promotional tools such as cross-market discounts have been increasingly used as a means to increase customer satisfaction and sales. This paper aims to assess whether the implementation of a cross-market discount campaign by a retailing company encouraged customers to increase their purchases level. It contributes to the literature by using neural networks to detect novelties in a real context involving cross-market discounts. Besides the computation of point predictions, the methodology proposed involves the estimation of neural networks prediction intervals. Sales predictions are compared with the observed values in order to detect significant changes in customers' spending. The use of neural networks is validated through the comparison with the forecasting estimates of support vector regression, regression trees, and linear regression. The results reveal that the promotional campaign under analysis did not significantly impact the sales of the rewarded customers. © 2019 John Wiley & Sons, Ltd.