The impact of COVID-19 pandemic on tourism employees: Was it the last straw?

Tourism, as one of the most vulnerable industries, has survived numerous global crises with substantial negative impact on economies, communities, businesses, and individuals. Despite the circumvention of the industry after those experiences of mild and severe crises, COVID-19 pandemic has been the most serious case with deep global impact in every corner of the world leading to the explosion of academic research on a plethora of pandemic aspects. However, research offering insights on tourism and hospitality employees' experiences, is scarce in the relevant literature in spite of the chronic problems of employee retention, qualified and long-term labor force. Therefore, the aim of this study addresses at examining the experiences of hotel employees in T & uuml;rkiye during and after COVID-19, which caused sudden and deep changes in the lives following the severe decline in tourism employment and economic problems it ushered in. The data was collected through in-depth interviews with 21 individuals who formerly worked in city or resort hotels at various positions and departments. Two sensemaking perspectives were integrated to find out the consequences of the pandemic leading to the causes and factors to end working in the industry. Study findings offer important insights into pandemic-related dynamics and could support the development of effective tourism policy and practices leading to improve crisis management efforts in the tourism and hospitality industry

Optimizing election logistics: A multi-period routing problem embedding time-dependent reward functions

With the 2024 US Presidential Election now concluded, the growing complexity of designing effective election campaigns has become clearer. Motivated by the logistical challenges associated with US election campaigns, we introduce the Reward-driven Multi-period Politician Routing Problem. It involves diverse politicians planning their campaigns over multiple days, considering constraints such as clustered locations, time-and location-dependent rewards, budget limits, mandatory rest days, and flexible daily routes that can be either open or closed, with starting and ending locations not known in advance. We model the problem as a mixed-integer linear program, complemented with several valid inequalities, and innovate by designing new subtour elimination techniques that jointly deal with open and closed paths. We developed 36 new benchmark instances tailored to the US presidential elections. To tackle large-sized instances, we develop a Sequential Route Construction Matheuristic that exploits the multi-period structure of the problem to provide efficient and effective solutions. We incorporate time-dependent reward profiles (concave, convex, linearly decreasing, linearly increasing, and periodic) into the objective function to capture diverse decision-making perspectives. Experimental results show interesting computational issues on the different tested models and the impact of the chosen reward profile on their performance.Publisher versio

Detecting credit card fraud by modified Fisher discriminant analysis

Due to copyright restrictions, the access to the full text of this article is only available via subscription.In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which somehow over-fit the dataset they are built on, one can expect simpler algorithms may show a more robust performance on a range of datasets. Although, linear discriminant functions are less complex classifiers and can work on high-dimensional problems like credit card fraud detection, they did not receive considerable attention so far. This study investigates a linear discriminant, called Fisher Discriminant Function for the first time in credit card fraud detection problem. On the other hand, in this and some other domains, cost of false negatives is very higher than false positives and is different for each transaction. Thus, it is necessary to develop classification methods which are biased toward the most important instances. To cope for this, a Modified Fisher Discriminant Function is proposed in this study which makes the traditional function more sensitive to the important instances. This way, the profit that can be obtained from a fraud/legitimate classifier is maximized. Experimental results confirm that Modified Fisher Discriminant could eventuate more profit.TÜBİTA