The Impact of Remote Work on Freelancing Careers in Digital Marketing: Work-Life Balance and Income Stability
Overview
This research proposal investigates how remote freelancing on digital platforms affects work-life balance and income stability for digital marketing professionals. The study proposes a mixed-methods approach combining quantitative surveys and earnings data from 40-60 freelancers with qualitative interviews to examine the paradox of improved work-life balance alongside reduced income stability in gig work.
What this research paper covers
- Research question and gap analysis addressing the lack of studies specific to digital marketing roles and AI tool integration
- Mixed-methods methodology combining quantitative Likert-scale surveys with earnings data and qualitative open-ended responses
- Literature review citing recent gig work research and proposed triangulation approach for combining statistical and contextual data
Preview
RE: Request for Feedback on Research Proposal
Dear Dr. Rosemary,
I hope this email finds you well. My name is Donald Ngonyo, and I am a student in ENGL 1102 at University of the People. I am currently developing a research project titled “The Impact of Remote Work on Freelancing Careers in Digital Marketing,” focusing on how remote freelancing affects work-life balance and income stability for professionals in this creative field.
My proposed research question is: To what extent does remote freelancing on digital platforms improve self-reported work-life balance for digital marketing freelancers while reducing income stability due to project inconsistency and algorithmic factors? The literature reveals consistent challenges in gig work (Gussek et al., 2023; Wood et al., 2018; Dunn et al., 2021), but there is a notable gap in studies specific to digital marketing roles and recent AI tool integration.
For this study, I believe a mixed-methods approach is most appropriate. Quantitative methods (structured surveys with Likert scales and earnings data from 40–60 freelancers via Upwork/LinkedIn) will allow measurable testing of the hypothesis on balance scores and income variability. Qualitative components (open-ended survey responses or follow-up interviews) will provide deeper insight into experiences with platform pressures and AI tools. This combination enables triangulation, combining statistical trends with contextual understanding, particularly valuable when exploring both measurable outcomes and subjective perceptions (Serdikoff, 2021).
I would greatly value your expert feedback or guidance on refining the methodology or identifying additional sources.
Thank you for considering this request.