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Health risks associated with coronavirus disease 2019 (COVID-19) have severely affected the financial stability of airline companies globally. Recapturing financial stability following this crisis depends heavily on these companies’ ability to attain efficient and productive operations. This study uses several empirical approaches to examine key factors contributing to carriers sustaining high productivity prior to, during and after a major recession. Findings suggest, regardless of economic conditions, that social distancing which requires airline companies in the Asia Pacific region to fly with a significant percentage of unfilled seats weakens the performance of those companies. Furthermore, efficient operations do not guarantee the avoidance of productivity declines, especially during a recession.

The accurate valuation of second-hand vessels has become a prominent subject of interest among investors, necessitating regular impairment tests. Previous literature has predominantly concentrated on inferring a vessel's price through parameter estimation but has overlooked the prediction accuracy. With the increasing adoption of machine learning for pricing physical assets, this paper aims to quantify potential factors in a non-parametric manner. Furthermore, it seeks to evaluate whether the devised method can serve as an efficient means of valuation.

This paper proposes a stacking ensemble approach with add-on feedforward neural networks, taking four tree-driven models as base learners. The proposed method is applied to a training dataset collected from public sources. Then, the performance is assessed on the test dataset and compared with a benchmark model, commonly used in previous studies.

The results on the test dataset indicate that the designed method not only outperforms base learners under statistical metrics but also surpasses the benchmark GAM in terms of accuracy. Notably, 73% of the testing points fall within the less-than-10% error range. The designed method can leverage the predictive power of base learners by incrementally adding a small amount of target value through residuals and harnessing feature engineering capability from neural networks.

This paper marks the pioneering use of the stacking ensemble in vessel pricing within the literature. The impressive performance positions it as an efficient desktop valuation tool for market users.

This research explores how contextual elements and significant events influence the changing storylines within a company's directors' reports spanning a period of six decades. These elements and events encompass the internal dynamics of the family that owns the company, industry-specific advancements, political and socioeconomic climates, and explicit guidelines related to corporate reporting.

This research employs a case study methodology to analyse the directors' reports of Barilla, a prominent Italian food manufacturer, within the theoretical framework of historical institutionalism. A systematic content analysis is conducted on sixty directors' reports published between 1961 and 2021. The study also identifies and examines significant contextual events within this six-decade period, which are linked to four key institutional factors.

Based on the research findings, the directors' reports exhibited notable fluctuations throughout the studied timeframe in reaction to shifts in the institutional setting. Our investigation highlights that each institutional element experienced crucial pivotal moments, and given their interconnected nature, modifications in one factor impacted the others. It was noted that these pivotal moments resulted in alterations in the directors' reports' content across various thematic areas. Additionally, despite Barilla being a multinational company, it was found that national events within Italy had a more pronounced influence on the evolving narratives than global events.

Previous research on directors' reports or chairman's statements has primarily focused on the influence of macro-level institutional factors on the narratives. In contrast, our study considers both macro-level and micro-level institutions, specifically examining the internal events within a family business and how they shape the content of directors' reports. Our study is also distinctive in its analysis of specific critical junctures and their interactions with the investigated institutional factors. Additionally, to the best of our knowledge, few existing studies span a timeframe of sixty years, particularly concerning an Italian company.

This study aims to perform a benefit segmentation and then a classification of visitors that travel to the Rocha Department in Uruguay from the capital city of Montevideo during the summer months.

A convenience sample was obtained with an online survey. A total of 290 cases were usable for subsequent data analysis. The following statistical techniques were used: hierarchical cluster analysis, K-means cluster analysis, machine learning, support vector machines, random forest and logistic regression.

Visitors that travel to the Rocha Department from Montevideo can be classified into four distinct clusters. Clusters are labelled as “entertainment seekers”, “Rocha followers”, “relax and activities seekers” and “active tourists”. The support vector machine model achieved the best classification results.

Implications for destination marketers who cater to young visitors are discussed. Destination marketers should determine an optimal level of resource allocation and destination management activities that compare both present costs and discounted potential future income of the different target markets. Surveying non-residents was not possible. Future work should sample tourists from abroad.

The combination of market segmentation of Rocha Department’s visitors from the city of Montevideo and classification of sampled individuals training various machine learning classifiers would allow Rocha’s destination marketers determine the belonging of an unsampled individual into one of the already obtained four clusters, enhancing marketing promotion for targeted offers.