Model of work motivation based on happiness: pandemic related study
Auteurs : Joanna Nieżurawska, Radosław A. Kycia, Iveta Ludviga, Agnieszka Niemczynowicz
Résumé : This study aims to enrich the current literature by providing a new approach to motivating Generation Z employees in Poland. Employees need to be motivated in order to be efficient at doing a particular task at the workplace. As young people born between 1995 and 2004 called Generation Z, enter the labour market it, is essential to consider how employees' motivation might be affected. Traditionally identified motivators are known, but many reports indicate that the motivation continues decreasing. This situation causes some perturbations in business and fluctuations of staff. In order to prevent this situation, the employers are looking for new solutions to motivate the employees. A quantitative approach was used to collect new evidence from 200 Polish respondents completing an online survey. The research were conducted before and during pandemic time. We report and analyse the survey results conducted in Poland among representatives of Generation Z, who were employed for at least 6 months. We developed and validated a new approach to motivation using methodologies called Factor Analysis. Based on empirical verification, we found a new tool that connects employee motivation and selected areas of the Hygge concept called Hygge star model, which has the same semantics before and during Covid-19 pandemic.
Explorez l'arbre d'article
Cliquez sur les nœuds de l'arborescence pour être redirigé vers un article donné et accéder à leurs résumés et assistant virtuel
Recherchez des articles similaires (en version bêta)
En cliquant sur le bouton ci-dessus, notre algorithme analysera tous les articles de notre base de données pour trouver le plus proche en fonction du contenu des articles complets et pas seulement des métadonnées. Veuillez noter que cela ne fonctionne que pour les articles pour lesquels nous avons généré des résumés et que vous pouvez le réexécuter de temps en temps pour obtenir un résultat plus précis pendant que notre base de données s'agrandit.