Hiring and Separation Rates Before and after the Arab Spring in the Tunisian Labour Market

We seek to explore the hiring and separation rates in Tunisia before and after the Arab Spring based on quarterly business level data for 503 firms over the span of January 2007 to December 2012. Furthermore, we examine whether employers are willing to dismiss older workers to trigger an effective increase in mobility that will open new opportunities for the youth community. We build our analysis upon six main empirical models to study employment decisions reflected by major indicators such as the number of hiring, number of separations, total employment effects, male‐female ratio, age cohorts, labour mobility and net employment. The results show that the Arab Spring has created structural unemployment trends. In addition, we note that the 2008 global turmoil has fostered the firing level of employment. Our conclusions also indicate that the response of Tunisia's government to high unemployment rates caused by the financial meltdown in 2008 and the events in 2011 was not sufficient to remove the attached lingering effects that still distress the country's labour market. In addition, our findings emphasize the significant challenges faced by Tunisian youth that could be mitigated by efficient policy actions to incentivize training and development geared towards the private sector.     

Is the tourism–economic growth nexus time-varying? Bootstrap rolling-window causality analysis for the top 10 tourist destinations

This article explores the time-varying causal nexus between tourism development and economic growth for the top 10 tourist destinations in the world, namely China, France, Germany, Italy, Mexico, the Russian Federation, Spain, Turkey, the UK and the United States of America, over the period 1990–2015. To that end, a bootstrap rolling window Granger causality approach based on the modified Granger causality test is used. A new index for tourism activity which combines via principal component analysis the commonly used tourism indicators is also employed. The results of the bootstrap rolling window causality tests reveal that the causal relations between tourism and economic growth vary substantially over time and across countries in terms of both magnitude and direction. It is shown that the causal linkages tend to be more pronounced for a large group of countries following the global financial crisis of 2008. Additionally, Germany, France and China clearly stand out as the countries with the weakest causal nexus, while the UK, Italy and Mexico emerge as the countries that have the strongest causal links. These results have particularly important implications for policymakers.     

E-Alliance: A Negotiation Infrastructure for Virtual Alliances

In this paper we provide an overview of E-Alliance, a software infrastructure we are developing to support negotiation activities in concurrent inter-organisational alliances. Our baseline is to offer a collaboration framework which fully preserves the autonomy of organisations grouped in an alliance, while enabling concurrency of their activities, flexibility of their negotiations and dynamic evolution of their environment. We propose to support negotiation between the partners within such alliances by combining different technologies, such as software engineering techniques, middleware-level coordination facilities and multiagent systems support. We present our approach in the context of a sample scenario of an alliance where partners are printshops capable of (out/in) sourcing print jobs among them to better accomplish their customers' requests.     

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.