Strategies Hospitality Leaders Use to Reduce Employee Turnover

Employee turnover is a global problem with adverse effects on financial performance and sustainability of organizations. In the hospitality industry, employee turnover levels increased to 58.8%, and the associated cost of turnover may be more than 100% of an employee\u27s yearly wage, with a total loss of over $25 billion a year. The purpose of this single case study was to explore strategies used by hospitality leaders in the southeastern United States to reduce employee turnover. The conceptual framework was the transformational leadership theory. Purposeful selection of participants included leaders with experience in developing and implementing strategies for reducing employee turnover. Data collection included face-to-face semistructured interviews with 8 organizational leaders and a review of declassified organizational documents. Data analysis included inductive coding and calculation of code frequency. Results indicated 3 themes: effective hiring process reduced employee turnover, supportive leadership decreased employee turnover, and continuous training and development reduced employee turnover. Reduced employee turnover may contribute to positive social change by saving organizations time, efforts, and resources, which organizational leaders may use to sustain growth and profitability and to improve the lives of their employees, their employees\u27 families, and the communities in which they operate

DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS

During the lifecycle of mega engineering projects such as: energy facilities, infrastructure projects, or data centers, executives in charge should take into account the potential opportunities and threats that could affect the execution of such projects. These opportunities and threats can arise from different domains; including for example: geopolitical, economic or financial, and can have an impact on different entities, such as, countries, cities or companies. The goal of this research is to provide a new approach to identify and predict opportunities and threats using large and diverse data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to inform domain specific foresights. In addition to predicting the opportunities and threats, this research proposes new techniques to help decision-makers for deduction and reasoning purposes. The proposed models and results provide structured output to inform the executive decision-making process concerning large engineering projects (LEPs). This research proposes new techniques that not only provide reliable timeseries predictions but uncertainty quantification to help make more informed decisions. The proposed ensemble framework consists of the following components: first, processed domain knowledge is used to extract a set of entity-domain features; second, structured learning based on Dynamic Time Warping (DTW), to learn similarity between sequences and Hierarchical Clustering Analysis (HCA), is used to determine which features are relevant for a given prediction problem; and finally, an automated decision based on the input and structured learning from the DTW-HCA is used to build a training data-set which is fed into a deep LSTM neural network for time-series predictions. A set of deeper ensemble programs are proposed such as Monte Carlo Simulations and Time Label Assignment to offer a controlled setting for assessing the impact of external shocks and a temporal alert system, respectively. The developed model can be used to inform decision makers about the set of opportunities and threats that their entities and assets face as a result of being engaged in an LEP accounting for epistemic uncertainty