The Happy Level: A New Approach to Measure Happiness at Work Using Mixed Methods
| Autor: | Gisela Sender, Flavio Carvalho, Gustavo Paiva Guedes |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Happiness at work
Sentiment analysis Measure (physics) 020207 software engineering 02 engineering and technology Education Narrative inquiry Holy Grail 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Job satisfaction lcsh:H1-99 lcsh:Social sciences (General) Psychology Social psychology |
| Zdroj: | International Journal of Qualitative Methods, Vol 20 (2021) |
| ISSN: | 1609-4069 |
| Popis: | Happiness at Work is considered the Holy Grail of organizational sciences. The belief that happier workers are more productive leads to a win-win situation for both individuals and organizations. Nevertheless, years of research have not brought a convergent conclusion about the topic, mainly due to the lack of a widely accepted measure. Usually, questionnaires and self-report surveys are used; however, these methods embed shortcomings that allow studies’ results to be questioned. In order to overcome these shortcomings, the present study proposes a different approach to measure Happiness at Work, bringing mixed methods to encompass the complexity of the phenomenon. Based on work-life narratives and following Kahneman’s concepts, the proposed approach puts together Narrative Analysis and Sentiment Analysis. Although increasingly used to assess social media reviews, Sentiment Analysis is not yet applied to narratives related to Happiness at Work. Four methods to calculate the Happy Level indicator were tested on actual research data: one manual, through traditional coding processes, and three automatic methods to provide scalability. An example of the Happy Level application is also provided to illustrate how the indicator could improve analyses. The present study concludes that despite the manual method presents better results at this moment; the automatic ones are promising. The results also indicate paths for improvement of these methods. |
| Databáze: | OpenAIRE |
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