Battlefield visitor motivations: explorations in the Great War town of Ieper,Belgium

In this study a quantitative method was used to measure the motivations for education, holiday and remembrance for an international sample of visitors to the Great War town of Ieper in Belgium. Three clusters could be formed based upon different structure and intensity of their motivations. A small number of visitors could be identified as pilgrims, while the remainder were tourists, and they evidenced most of the characteristics of the pilgrims. The theory of thanatourism provides a framework through which the motivations and experiences of battlefield tourists for remembrance and education as well as leisure can be better understood. Copyright © 2010 John Wiley & Sons, Ltd.     

Capital and labor misallocation in the Netherlands

Journal of Productivity Analysis - Using firm-level panel data we analyze the misallocation of capital and labor for the Netherlands in the period 2001–2017. We use the dispersion in marginal...     

Managing the retail operations in the COVID-19 pandemic: Evidence from Morocco

Novel coronavirus disease (COVID-19) and resulting lockdowns have contributed to major retail operational disturbances around the globe, forcing retail organizations to manage their operations effectively. The impact can be measured as a black swan event (BSE). Therefore, to understand its impact on retail operations and enhance operational performance, the study attempts to evaluate retail operations and develop a decision-making model for disruptive events in Morocco. The study develops a three-phase evaluation approach. The approach involves fuzzy logic (to measure the current performance of retail operations), graph theory (to develop an exit strategy for retail operations based on different scenarios), and ANN and random forest-based prediction model with K-cross validation (to predict customer retention for retail operations). This methodology is preferred to develop a unique decision-making model for BSE. From the analysis, the current retail performance index has been computed as “Average” level and the graph-theoretic approach highlighted the critical attributes of retail operations. Further, the study identified triggering attributes for customer retention using machine learning-based prediction models (MLBPM) and develops a contactless payment system for customers' safety and hygiene. The framework can be used on a periodic basis to help retail managers to improve their operational performance level for disruptive events.