A Qualitative Phenomenological Study Of Inner Speech Used As A Self-Help Tool Among Adult Remote Workers

<p>The COVID-19 pandemic and subsequent shift to remote work emphasized the importance of maintaining the well-being of adult remote workers. The problem studied was the awareness, understanding, and acceptance of inner speech/self-talk among adult remote workers. The purpose of this qualitative, phenomenological study was to explore the perceptions and experiences of adult remote workers regarding their use of inner speech/self-talk. Inner speech/self-talk is an innate ability of higher mental functions that include reasoning, problem solving, planning and plan execution, attention, and motivation. Data collection involved the purposeful sampling of 10 participants who were adult remote workers. The semistructured interviews were conducted via Google Meet and were transcribed using Otter software. The data were manually coded, and they revealed three interconnected themes: the state of consciousness, self-regulation, and social interaction. The participants shared insights into the applications of inner speech and self-talk from coping with isolation and adapting to communication styles in remote work to managing emotions, enhancing self-discipline, and planning interactions. In conclusion, this study’s implications extended to individuals, communities, and organizations. The study illuminated inner speech and self-talk functions in the lives of adult remote workers.</p>

Name thesis:degree_name

Doctor of Education (EdD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Education

Author dc:creator

• Yeung, Jennie, Dr.

Contributors dc:contributor

• Ella Benson
• Evelyn Thomas

• inner speech
• self-talk
• remote workers
• self-regulation
• state of consciousness
• Education
• Other Social and Behavioral Sciences
• Psychology
• Social and Behavioral Sciences
• Social Psychology

Repository record dc:identifier

https://dune.une.edu/edu_diss/33

OAI identifier oai:identifier

oai:dune.une.edu:edu_diss-1036