Predictive Analytics im Human Capital Management : Status Quo und Potentiale

First Online: 23 December 2015 Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)Unternehmen verfügen mittlerweile über fortgeschrittene analytische Informationssysteme, die es erlauben die wachsenden Datenmengen nahezu automatisiert auszuwerten und Aussagen über zukünftige Entwicklungen zu treffen. Predictive Analytics befinden sie sich im Human Capital Management noch in den Anfängen. Datengetriebene Unternehmen wie Google oder Hewlett-Packard nutzen Predictive Analytics bereits, um Personalbeschaffung und -erhaltung zu verbessern. Obwohl jedoch die Personalbereiche über umfangreiche Daten verfügen, beschränkt sich deren Nutzung nach unserer Erfahrung und einer von uns durchgeführten Befragung in den meisten Fällen immer noch auf reaktives Excel-Reporting und einfachste Prognosen z. B. zur Personalanzahl. Data Mining-Verfahren werden hingegen selten genutzt, obwohl sich daraus für das Human Capital Management und andere Unternehmensbereiche Vorteile ergeben könnten. In diesem Beitrag stellen wir anhand von Praxisbeispielen und ausgewählter Fachliteratur Potentiale von Predictive Analytics im Human Capital Management vor, untersuchen die Verbreitung sowie die Einsatzmöglichkeiten von personalbezogenen Analysen und gehen auch auf die spezifischen Herausforderungen der Nutzung von Personaldaten ein

A two-stage analytical approach to assess sustainable energy efficiency

Administrators and policymakers at regional, national and global level are well aware of the necessity and undeniable benefits of renewable energy for long-term sustainability. In this study, we developed a two-stage analytical methodology to assess the efficiency of energy sources (a combination of various energy sources, mostly based on renewable sources), and Turkey, a country with a variety of renewable energy potential because of its favorable geographic and climatic conditions, was used as an illustrative case. Specifically, in the first stage, we utilized a nonparametric method and a powerful benchmarking tool—Data Envelopment Analysis (DEA)—to analyze energy efficiencies for each province. In the second stage, we employed the Ordinary Least Square (OLS) regression and Tobit regression models to investigate the environmental factors affecting energy efficiency. And then, we used the Charnes-Cooper-Rhodes (CCR) DEA and Tobit regression combination to perform a validation of the findings. The tandem utilization of DEA, OLS, and Tobit regression models allowed us to overcome some of the shortcomings of these methods when they are utilized individually. The results revealed the factors that have direct and positive influence/effect on the efficiencies, which included gross domestic product per-capita, population size, and the amount of energy production from renewable energy sources. The findings also suggested that starting the investments at the less-efficient provinces result in a better overall nationwide technical efficiency. These results can potentially help decision makers to develop and manage energy investment strategies

A two‐stage Bayesian network model for corporate bankruptcy prediction

We develop a Bayesian network (LASSO-BN) model for firm bankruptcy prediction. We select fnancial ratios via the Least Absolute Shrinkage Selection Operator (LASSO), establish the BN topology, and estimate model parameters. Our empirical results, based on 32,344 US firms from 1961-2018, show that the LASSO-BN model outperforms most alternative methods except the deep neural network. Crucially, the model provides a clear interpretation of its internal functionality by describing the logic of how conditional default probabilities are obtained from selected variables. Thus our model represents a major step towards interpretable machine learning models with strong performance and is relevant to investors and policymakers

Dynamic temporary blood facility location-allocation during and post-disaster periods

The key objective of this study is to develop a tool (hybridization or integration of different techniques) for locating the temporary blood banks during and post-disaster conditions that could serve the hospitals with minimum response time. We have used temporary blood centers, which must be located in such a way that it is able to serve the demand of hospitals in nearby region within a shorter duration. We are locating the temporary blood centres for which we are minimizing the maximum distance with hospitals. We have used Tabu search heuristic method to calculate the optimal number of temporary blood centres considering cost components. In addition, we employ Bayesian belief network to prioritize the factors for locating the temporary blood facilities. Workability of our model and methodology is illustrated using a case study including blood centres and hospitals surrounding Jamshedpur city. Our results shows that at-least 6 temporary blood facilities are required to satisfy the demand of blood during and post-disaster periods in Jamshedpur. The results also show that that past disaster conditions, response time and convenience for access are the most important factors for locating the temporary blood facilities during and post-disaster periods

How teachers' practices and students' attitudes towards technology affect mathematics achievement: results and insights from PISA 2012

The present work seeks to deepen the impact of factors linked to the characteristics of teaching practices and students' attitudes towards the use of technology on their performance in mathematics in the process of teaching-learning in the Spanish context. In this sense, this study is a secondary analysis of the PISA 2012 data. Therefore, it is an ex post facto design. Regarding the attitudes and the contextual variables, the results do coincide with the accumulated evidence. However, once these contextual effects have been controlled for, the negative relationship found between the pedagogic strategies used by the teachers and the mathematics score cannot but convey perplexity, since the results relative to student-oriented, formative assessment and teacher-directed instruction are clearly contradictory to the solid previous evidence. The data do not allow us to explain this paradoxical result. We dare to point to a conjecture that we find plausible. All these complex variables are informed through questionnaires responded to by students and require a great degree of inference in the answers. Future studies must consider the complexity of the measured variables as well as the students' perception and understanding of them

Using Data-mining Techniques for the prediction of the severity of road crashes in Cartagena, Colombia

Objective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents from 2016 to 2017. The severity was determined between low (84%) and high (16%). Five classification algorithms to predict the accident severity were applied with WEKA Software (Waikato Environment for Knowledge Analysis). Including Decision Tree (DT-J48), Rule Induction (PART), Support Vector Machines (SVMs), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each algorithm was implemented using cross-validation with 10-fold. Decision rules were defined from the results of the different methods. Results: The methods applied are consistent and similar in the overall results of precision, accuracy, recall, and area under the ROC curve. Conclusions: 12 decision rules were defined based on the methods applied. The rules defined show motorcyclists, cyclists, including pedestrians, as the most vulnerable road users. Men and women motorcyclists between 20–39 years are prone in accidents with high severity. When a motorcycle or cyclist is not involved in the accident, the probable severity is low