The opportunity cost of social relations: On the effectiveness of small worlds

The aim of this paper is to extend the theoretical literature on knowledge and network structure by studying the use of social networks as a learning mechanism. The novelty of this approach is suggested by the empirical evidence on informal trading of know-how. In the model, we consider a set of actors who create and diffuse knowledge with the aim of increasing their own personal knowledge. They are located on a lattice (identifying the social space) and are directly connected to a small number of other individuals. We assume that individuals can learn individually or socially, and that individuals choose how to learn on the basis of a cost-benefit comparison. Within this framework, we compare network structures in terms of efficiency and equity. We find that the opportunity cost of using the network affects its optimal structure in terms of aggregate performance and that the small world does not emerge unambiguously as being the most efficient.     

Uncovered Interest Parity at Long Horizons: Evidence on Emerging Economies*

This paper estimates uncovered interest parity (UIP) at long horizons using bilateral US dollar rates vis‐à‐vis mature economy and emerging market currencies. The paper finds support in favor of UIP for dollar rates vis‐à‐vis major mature economy currencies, but far less against emerging market currencies. There are also signs that political risk and the exchange risk premium help explain the empirical failure of UIP for these latter currencies. This suggests that whether UIP holds depends more on the currency than on the horizon.     

Big data in smart tourism: challenges,issues and opportunities

In the era of digital transformation, Big Data have assumed a crucial role in changing the global travel and providing significant challenges and opportunities for established companies, as well as new entrants into the tourism industry. All these companies can get valuable information on Big Data for predicting tourist demand, enabling better decision-making, managing knowledge flows and interaction with customers, and providing the best service in a more efficient and effective way. This can result in improved productivity, increased customer satisfaction, personalized marketing campaigns, and more efficient operations. However, open research issues about the role of Big Data in the tourism industry can be still recognized. With these premises, this Editorial aims to present the articles included in the special issue of Current Issues in Tourism titled ‘Big data in smart tourism: challenges, issues and opportunities’, which has called for research contributing to the recent debates on the implications and challenges of the adoption of Big Data to improve the competitiveness of tourism destinations and companies. Main topics considered by the accepted articles include a literature review proposing a novel theoretical investigative frameworks, metrics and critical dimensions, and empirical investigations of the use of Big Data in different tourism contexts.     

Transport biofuels in the European Union: The state of play

The implementation of the Biofuels Directive (2003/30/EC) in 25 member states is assessed here. Implementation problems are identified and analysed for the purpose of suggesting a way forward for biofuels policy in the European Union. Three main patterns are observed: effective implementation, formal compliance and weak implementation. Problems encountered in the policy process are both of a practical and more fundamental nature, i.e., due to a lack of consensus on the priority of competing policy objectives and disagreement on the suitability of first-generation biofuels for reaching the objectives. In this situation, stronger enforcement of targets does not appear to be a constructive way forward. Based on our analysis, we argue that the European Union should instead, and in agreement with the principle of subsidiarity, allow member states more flexibility.     

An evaluation of the performance and suitability of R × C methods for ecological inference with known true values

Ecological inference refers to the study of individuals using aggregate data and it is used in an impressive number of studies; it is well known, however, that the study of individuals using group data suffers from an ecological fallacy problem (Robinson in Am Sociol Rev 15:351–357, 1950). This paper evaluates the accuracy of two recent methods, the Rosen et al. (Stat Neerl 55:134–156, 2001) and the Greiner and Quinn (J R Stat Soc Ser A (Statistics in Society) 172:67–81, 2009) and the long-standing Goodman’s (Am Sociol Rev 18:663–664, 1953; Am J Sociol 64:610–625, 1959) method designed to estimate all cells of R × C tables simultaneously by employing exclusively aggregate data. To conduct these tests we leverage on extensive electoral data for which the true quantities of interest are known. In particular, we focus on examining the extent to which the confidence intervals provided by the three methods contain the true values. The paper also provides important guidelines regarding the appropriate contexts for employing these models.     

The “Bologna Process” and college enrollment decisions

We use survey data on high school graduates before and after the Italian reform of tertiary education implementing the “Bologna Process” to estimate its impact on college enrollment. Individuals leaving school after the reform have a probability of enrollment that is 15% higher compared to otherwise identical individuals. This increase is concentrated among individuals with good school performance and low parental background. We interpret this result as an indication of the existence of constraints – for good students from less advantaged households – on the optimal schooling decision. We also find a small negative impact of the reform on university drop-out.     

Postponement Strategies for Global Downstream Supply Chains: A Conceptual Framework

Postponement is a well-known organizational concept and usually relates to the deferment in time of manufacturing and/or logistics operations. In the current global competitive landscape, situations where postponement can be applied are rapidly increasing. Faced with the wide range of customs duties and free-trade agreements currently in place, companies need to (re)design their postponement strategies to customize their products appropriately, and to the proper degree, in each market. As the actual location where operations take place has a major impact on a company’s overall performance, the spatial perspective must also be taken into account when designing global postponement strategies, alongside the conventional temporal perspective. Heretofore, the academic literature does not offer any comprehensive framework on a global scale either for handling what is known as the postponement boundary problem, or for conceptualizing the related postponement strategies. Building on previous research, the aim of the present study was to investigate the postponement concept in a global environment with a downstream focus. The intended purpose is twofold: first, to review and expand previous studies on the subject and, second, to provide some guidelines for conceptualizing global postponement strategies. A structured literature review was first conducted, followed by the development of a framework that combines both the temporal and the spatial dimensions. Finally, the framework was applied to a group of 28 business cases taken from the literature, to act as a bridge between academic theories and practitioners’ current business operations.