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This study aims to identify the impact of foreign direct investment (FDI) on economic growth in Morocco depending on each origin country, including Spain. This study uses a linear model to measure the marginal impact of FDI on the growth of Morocco. This marginal effect allows to compare the different effects of FDI among countries of origin. Also, the marginal effect helps to measure the rate of substitution between FDI in an easier way than the other specifications of the model. The second step determines the substitute for Spain in case he decides to divest its FDI from Morocco to maintain the economic growth.

Using data of FDI from 13 countries of origin from 1995 to 2020 and two estimation methods (Dynamic Ordinary Least Squares and Autoregressive model), this study aims to measure the marginal impact of the divestment of FDI from Spain on growth. Then this study estimates how much Morocco should attract FDI from other countries when Spain divests. This study uses the differential calculus, assuming a perfect substitution between FDI from different countries. This calculus implies an indifference curve between FDI from Spain and FDI from another country where we deduct the substitution rates between FDI.

The results indicate that the FDI from Spain and France are the only ones to impact positively Moroccan economic growth. The FDI coming from Germany, Holland, China and Turkey have a negative impact, whereas those from the USA, Italy, UK, Switzerland and Gulf countries: Saudi Arabia, Kuwait and UAE have an insignificant effect. Second, using the differential calculus, the result indicates that when Spain divests 1m dirhams of its investments from Morocco, France would have to increase its own by 0.1509m dirhams so that Morocco could maintain its economic growth.

The research focuses only on economic growth, neglecting the impact on other aggregates, such as total factor productivity, technology transfer and employment. Also, this research marginalized the sectorial analysis of FDI by the source to better understand the divergent effects.

This paper fills a research gap when analyzing the effect of FDI on the host economy depending on country-of-origin. In addition, it contributes to the body of literature by constructing the rate of substitution between the different sources of FDI to adapt to divestment policy.

In recent years, international bodies and public opinion have recommended that governments adopt social responsibility practices to inform and be accountable to citizens about their sustainability actions in environmental, social and economic fields (Galera et al., 2014) and restore citizens' confidence in public authorities (Crane et al., 2008; Shepherd et al., 2010). This chapter reviews the literature on measuring and reporting sustainable performance in the public sector. Analyzing 35 studies published in a period of 10 years (from 2012 to 2021), we address two specific research questions: How and to what extent have public organizations changed to integrate sustainability reporting (SR) systems? What are the enabling organizational factors in adopting SR in public organizations?

Although prior research has employed various variables to predict player churn, the dynamic evolution of the behavioral patterns of players has received limited attention. In this study, churn prediction models are developed by incorporating the progress level, in-game purchase, social interaction, behavioral pattern and behavioral variability (BV) of players in social casino games (SCGs). The study distinguishes churn prediction between two player groups: monetizers and non-monetizers.

This study employs three machine learning techniques—logistic regression, decision trees and random forests—using real-world player data from an SCG company to construct churn prediction models. Two experiments were conducted. In Experiment 1, BV was combined with four other variable categories to effectively predict churn behaviors across all players (n = 52,246). In Experiment 2, churn prediction models were developed separately for monetizers (n = 16,628) and non-monetizers (n = 35,618).

The findings from Experiment 1 indicate that incorporating BV significantly improves the overall performance of churn prediction models. Experiment 2 demonstrates that churn prediction models achieve better performance and predictive accuracy for monetizers and non-monetizers when BV is calculated over the 3-day to 7-day and 7-day to 14-day windows, respectively.

This study introduces BV as a novel variable category for churn prediction, emphasizing within-person variability and demonstrating its effectiveness in enhancing model performance. Churn prediction models were independently constructed for monetizers and non-monetizers, utilizing different time windows for variable extraction. This approach improves predictive performance and highlights key differences in critical variables influencing churn across the two player groups. The findings provide valuable insights into churn management strategies tailored for monetizers and non-monetizers.

The purpose of this paper is to develop a mathematical model that determines the location of temporary logistics hubs (TLHs) for disaster response and proposes a new method to determine weights of the objectives in a multi-objective optimization problem. The research is motivated by the importance of TLHs and the complexity that surrounds the determination of their location.

A multi-period multi-objective model with multi-sourcing is developed to determine the location of the TLHs. A fuzzy factor rating system (FFRS) under the group decision-making (GDM) condition is then proposed to determine the weights of the objectives when multiple decision makers exist.

The interview with decision makers shows the heterogeneity of decision opinions, thus substantiating the importance of GDM. The optimization results provide useful managerial insights for decision makers by considering the trade-off between two non-commensurable objectives.

In this study, decision makers are considered to be homogeneous, which might not be the case in reality. This study does not consider the stochastic nature of relief demand.

The outcomes of this study are valuable to decision makers for relief distribution planning. The proposed FFRS approach reveals the importance of involving multiple decision makers to enhance sense of ownership of established TLHs.

A mathematical model highlighting the importance of multi-sourcing and short operational horizon of TLHs is developed. A new method is proposed and implemented to determine the weights of the objectives. To the best of the authors’ knowledge, the multi-actor and multi-objective aspects of the TLH location problem have not thus far been considered simultaneously for one particular problem in humanitarian logistics.

This study aims to provide a method to assess the perceptual impact of the visual complexity of building façades.

The research identifies the number of design elements and the variation in their position and colour as variables of visual complexity. It introduces the concepts of vertices and corners as atomic indicators on which the measurement of these variables is built. It measures visual complexity and its variables in images of building façades and analyses their relationships with participants' reactions. It reports on the effect of visual complexity on preferences, the adequacy of different methods in measuring visual complexity and the perceptual impact of each of its variables.

The research demonstrates that visual complexity can be assessed through the measure of its variables and their statistical mapping to users' preferences.

The manuscript provides the foundation for a planning/assessment tool for the visual control of the built environment using computer systems based on the preferences of residents through the examination of the relationship between the users and their environment. It creates a paradigm, which introduces a robust concept in the visual analysis of urban design.