PurposeThe main goal of employee retention is to prevent competent employees from leaving the company. When analysing the main reasons why employees leave and determining their turnover probability, the question arises: Which retention strategies have an actual effect on turnover and for which profile of employees do these strategies work?Design/methodology/approachTo determine the effectiveness of different retention strategies, an overview is given of retention strategies that can be found in the literature. Next, the paper presents a procedure to build an uplift model for testing the effectiveness of the different strategies on HR data. The uplift model is based on random forest estimation and applies personal treatment learning estimation.FindingsThrough a data-driven approach, the actual effect of retention strategies on employee turnover is investigated. The retention strategies compensation and recognition are found to have a positive average treatment effect on the entire population, while training and flexibility do not. However, with personalised treatment learning, the treatment effect on the individual level can be estimated. This results in an ability to profile employees with the highest estimated treatment effect.Practical implicationsThe results yield useful information for human resources practitioners. The personalised treatment analysis results in detailed retention information for these practitioners, which allows them to target the right employees with the right strategies.Originality/valueEven though the uplift modelling approach is becoming increasingly popular within marketing, this approach has not been taken within human resources analytics. This research opens the door for further research and for practical implementation.

Purpose This paper aims to question whether the available data in the human resources (HR) system could result in reliable turnover predictions without supplementary survey information. Design/methodology/approach A decision tree approach and a logistic regression model for analysing turnover were introduced. The methodology is illustrated on a real-life data set of a Belgian branch of a private company. The model performance is evaluated by the area under the ROC curve (AUC) measure. Findings It was concluded that data in the personnel system indeed lead to valuable predictions of turnover. Practical implications The presented approach brings determinants of voluntary turnover to the surface. The results yield useful information for HR departments. Where the logistic regression results in a turnover probability at the individual level, the decision tree makes it possible to ascertain employee groups that are at risk for turnover. With the data set-based approach, each company can, immediately, ascertain their own turnover risk. Originality/value The study of a data-driven approach for turnover investigation has not been done so far.

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The implementation of autonomous delivery solutions in last-mile logistics operations is considered promising. Autonomous delivery solutions can help in tackling urban challenges related to last-mile logistics operations. Urbanization creates higher mobility and transportation demand, which contributes to increased congestion levels, traffic, air pollution, and accident rates. Moreover, mega-trends, such as e-commerce, demand that logistics companies react to increased customer expectations in terms of delivery time and service. Concerning service, electrified autonomous delivery solutions have the potential to operate 24/7 and can help to overcome driver shortages. This paper conducts a systematic literature review. Based on the literature set, a snowballing procedure was applied. Complementary gray literature was included. This work discusses different autonomous delivery solutions such as Autonomous Delivery Robots (ADRs), Unmanned Aerial Vehicles (UAVs), two- or multi-tiered systems, and the concept of passenger and freight integration. The work presents advantages and disadvantages, enabling the comparison of solutions. Furthermore, a research agenda is provided, from which practical-managerial and theoretical implications can be derived. The research agenda can help researchers, manufacturers, businesses, and governmental institutions to prepare for the arrival and subsequent implementation of autonomous delivery services. Various implications related to energy demand, legislation, implementation strategy, training, and risk and safety are presented. The outcome of this work calls for collaboration among various stakeholders, encourages mutual learning, and hints at the importance of national and international development projects.