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L'istruzione professionale nell'industria alberghiera si trova oggi in piena trasformazione. Le forme finora praticate non sono più sufficienti. Si devono trovare e seguire nuove vie. Il tempo preme. Si devono prendere rapidamente nuove misure se non si vuole che si formino lacune pericolose.

In Italia si è fatto quanto non è stato fatto in nessun altro Paese, sempre nel settore dell'istruzione media turistico‐alberghiera. Per cui mi permetto di suggerire, come ebbi a fare altre volte, di esaminare gli ordinamenti scolastici esistenti negli altri Paesi. Ciò per stabilire, in quanto è possibile, un programma comune sull'istruzione media turistica.

The purpose of this paper is to study the effect of different parts (predictable and impact) of different types of speculative behavior (intraday speculation, medium-term speculation and long-term speculation) on future fluctuations in the underlying index.

The authors input information about heterogeneous speculative behavior into the HAR-RV model to study the effect of different parts (predictable and impact) of different types of speculative behavior (intraday speculation, medium-term speculation and long-term speculation) on the future fluctuation of the underlying index.

The authors find that the increase in intraday speculation will exacerbate spot market volatility; and the expected increase of long-term value speculation can reduce market volatility, but the shock of speculation will exacerbate market volatility.

The authors suggest that regulators should strictly limit speculative intraday trading, and also focus on the long-term value speculation that decreases market volatility, in order to guide the benign development of the markets that stabilize abnormal market fluctuations.

First, in view of the correlation between the futures and spot markets, the authors put forward a new proxy for the speculation degree. Second, the authors input heterogeneous speculative behavior into the HAR-RV model to study the effects of different parts (predictable and impact) on different types of speculative behavior (intraday speculation, medium-term speculation and long-term speculation) on the future fluctuation of the underlying index.

This paper aims to apply the heterogeneous autoregressive model of realized volatility (HAR-RV) model to minimum-variance hedge ratio estimation and compares the hedging performance of presenting a model with that of a conventional rolling ordinary-least-square (OLS) hedging model. Moreover, this paper empirically analyzes the relationship between hedging performance and the heterogeneity of investors with different trading frequency in forming the expectation for the spot volatility, futures volatility and the covariance in the market.

Use HAR-RV to form expectations of participants of spots and futures market for the next period volatility based on two parts. One is the current observable realized volatility at the same time scale. The other is the expectation for the next longer time scale horizon volatility. Compare hedging performance with rolling OLS model and HAR-RV model. Present a three-times-scale-length (daily, weekly and monthly) HAR-RV model for the spot and futures returns and volatility to analyze the relationship between the hedging performance and the heterogeneity among participants in each market.

The empirical results show that HAR-RV model outperforms the rolling OLS in terms of variance reduction and expected utility in the out-of-sample period. The results also indicate that the greater variance reduction occurs when investors with different trading frequency have a less heterogeneous expectation for spot volatility and more heterogeneous expectation for futures volatility and the covariance. In addition, the expected utility increases along with lower heterogeneity in spot volatility and higher in futures volatility and the covariance. Hedging performance improves along with decreasing heterogeneity of investors in spot volatility and increasing heterogeneity in futures volatility and the covariance.

This paper considers the heterogeneity of participants in spot and futures market, the authors apply HAR-RV model to MVHR estimation and compare the hedging performance of presenting a model with that of conventional rolling OLS hedging model, providing more evidence in hedging literature. This paper analyzes in depth the relationship between hedging performance and the heterogeneity in the market.

The purpose of our study is to investigate how a manufacturing plant’s internal operations along with its network of connections (upstream and downstream) can have an impact on its recovery time from a disruption. The authors also examine the inverse-U impact of complexity. Finally, the authors test the moderating role that business continuity management plans (BCP) at the plant level have on recovery time.

To test our hypotheses, the authors partnered with Resilinc Corporation, a Silicon Valley-based provider of supply chain risk management solutions to identify focal firms’ suppliers, customers and plant-level data including information on parts, manufacturing activities, bill of materials, alternate sites and formal business continuity plans. The authors employed censored data regression technique (Tobit).

Several important findings reveal that the plant’s internal operations and network connections impact recovery time. Specifically, the number of parts manufactured at the plant as well as the number of internal plant processes significantly increase disruption recovery time. In addition, the number of supply chains (upstream and downstream) involving the plant as well as the echelon distance of the plant from its original equipment manufacturer significantly increase recovery time. The authors also find that there exists an inverted-U relationship between complexity and recovery time. Finally, the authors find partial support that BCP will have a negative moderating effect between complexity and recovery time.

This research highlights gaps in the literature related to supply chain disruption and recovery. There is a need for more accurate methods to measure recovery time, more research on recovery at the supply chain site level and further analysis of the impact of supply chain complexity on recovery time.

In this paper, the authors introduce supply disruption ambiguity as the inability of a sourcing firm to attach probability point estimates to the occurrence of and to the magnitude of loss from supply disruptions. The authors drew on the “ambiguity in decision-making” literature to define this concept formally, connected it to relevant supply disruption information deficit, positioned it relative to supply chain risk assessment and hypothesized and tested its negative associations with both supply base ties and inventory turnover.

The authors analysed survey data from 171 North American manufacturers and archival data for a subset (88 publicly listed) of these manufacturers via Ordinary Least Squares (OLS) estimation after ensuring that methodological concerns with survey research have been addressed. They used appropriate controls and employed the heteroskedasticity-based instrumental variable (HBIV) approach to ensure that inferences from our results are not unduly influenced by endogeneity.

Strong supply base ties decrease supply disruption ambiguity, which, in turn, increases inventory turnover. Moreover, strong supply base ties and data integration with the supply base have indirect and positive effects on inventory turnover. As sourcing firms strengthen ties and integrate data exchange with their supply base, their inventory turnover improves from access to information relevant to detect and diagnose supply disruptions effectively.

Research on supply disruption management has paid more attention to the “disruption recovery” stage than to the “disruption discovery” stage. In this paper, the authors add novel insights regarding the recognition and diagnosis aspects of the “disruption discovery” stage. These novel insights reveal how and why sourcing firms reduce their overall ambiguity associated with detecting and assessing losses from supply disruptions through establishing strong ties with their supply base and how and why reducing such ambiguity improves inventory turnover performance.

Severe disruptions to maritime supply chains, including port closures, congestion and shortages in shipping capacity, have occurred during the COVID-19 pandemic. This paper’s purpose is to explore flexibility-based countermeasures that enable actors in maritime supply chains to mitigate the effects of disruptions with different characteristics.

Semi-structured interviews were conducted with shipping lines, shippers, forwarders and ports. Data on the COVID-19 pandemic's effects and countermeasures were collected and compared with data regarding the 2016–2017 Gothenburg port conflict.

Spatial, capacity, service and temporal flexibility emerged as the primary countermeasures, whilst important characteristics of disruptions were geographical spread, duration, uncertainty, criticality, the element of surprise and intensity. Spatial flexibility was exercised in both disruptions by switching to alternative ports. During the COVID-19 pandemic, ensuring capacity flexibility included first removing and then adding vessels. Shipping lines exercising service flexibility prioritised certain cargo, which made the spot market uncertain and reduced flexibility for forwarders, importers and exporters that changed carriers or traffic modes. Experience with disruptions meant less surprise and better preparation for spatial flexibility.

Understanding how actors in maritime supply chains exercise flexibility-based countermeasures amid disruptions with different characteristics can support preparedness for coming disruptions.

Comparing flexibility-based measures in a pandemic versus port conflict provides insights into the important characteristics of disruptions and the relevance of mitigation strategies. The resilience of maritime supply chains, although underexamined compared with manufacturing supply chains, is essential for maintaining global supply chain flows.