Abstract
Purpose Elevating the human experience (HX) through research collaborations is the purpose of this article. ServCollab facilitates and supports service research collaborations that seek to reduce human suffering and improve human well-being. Design/methodology/approach To catalyze this initiative, the authors introduce ServCollab's three human rights goals (serve, enable and transform), standards of justice for serving humanity (distributive, procedural and interactional justice) and research approaches for serving humanity (service design and community action research). Research implications ServCollab seeks to advance the service research field via large-scale service research projects that pursue theory building, research and action. Service inclusion is the first focus of ServCollab and is illustrated through two projects (transformative refugee services and virtual assistants in social care). This paper seeks to encourage collaboration in more large-scale service research projects that elevate the HX. Practical implications ServCollab seeks to raise the aspirations of service researchers, expand the skills of service research teams and build mutually collaborative service research approaches that transform human lives. Originality/value ServCollab is a unique organization within the burgeoning service research community. By collaborating with service researchers, with service research centers, with universities, with nonprofit agencies and with foundations, ServCollab will build research capacity to address large-scale human service system problems. ServCollab takes a broad perspective for serving humanity by focusing on the HX. Current business research focuses on the interactive roles of customer experience and employee experience. From the perspective of HX, such role labels are insufficient concepts for the full spectrum of human life.

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.

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Abstract
It is imperative that online companies have a complete in-depth understanding of online behavior in order to provide a better service to their customers. This paper proposes a model for real-time basket addition in the e-grocery sector that includes predictors inferred from anonymous clickstream data, such as a Markov page view sequence discrimination value. This model aims at anticipating the addition and the non-addition of items to customers' market basket, in order to enable marketers to act conveniently, for example recommending more appropriate items. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of anonymous clickstream data taken from the servers of a European e-retailing company is explored. The empirical results reveal the high predictive power of the model proposed, based on the explanatory variables introduced, as well as the supremacy of random forests over logistic regression.

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The performance evaluation of education systems is at the top of the agenda of governments and education authorities worldwide. However, research involving cross-country comparisons of the performance of education systems is still incipient. This paper proposes a new composite indicator to summarise the performance of education systems, enabling benchmarking comparisons and the definition of objectives for improvement. The research analyses different modelling alternatives for the construction of composite indicators, with varying degrees of weight flexibility. Our study uses annual data of 29 European countries, collected from Eurostat and the Organisation for Economic Co-operation and Development databases. The results obtained in terms of performance scores and country rankings are presented and their managerial implications are discussed. We conclude that composite indicators estimated using frontier techniques can support the transition from the paradigm of performance assessment (control) to performance management (improvement).

Abstract
The performance of secondary schools is usually assessed based on students' results on national exams at the end of secondary education. This research uses data on academic achievements by first-year university students to benchmark secondary schools on their ability to lead students to success in higher education. The analysis is conducted using data of University of Porto and Catholic University of Porto, Portugal, for a three-year period, corresponding to more than 10.000 students from 65 degrees, for which the school of origin is known. A number of variables representing students' success in Higher education were constructed for each school in our sample and aggregated through a Benefit of the Doubt indicator. Results suggest that the schools' ranking based on schools' ability to prepare students for university success is quite different from the ranking based on results on national exams. Given these findings, we propose complementing schools' performance assessments (traditionally based on national exam results or indicators of value added) with indicators that account for the preparation of students for success in future challenges, which is indisputably a key objective of secondary education. We propose a composite indicator for the analysis of these complementary aims as well, and results show that frontier units indeed exhibit trade offs between traditional measures of performance and our new measure of performance.