Work Description

Title: Designing Future Employment Applications for Underserved Job Seekers: A Speed Dating Study Open Access Deposited

h
Attribute Value
Methodology
  • We gathered insights from past literature and generated ten tangible design concepts to address the needs of underserved job seekers. We then conducted a needs validation and speed dating study to understand which concepts were most viable among our population. Audio was recorded and professionally transcribed.
Description
  • Today’s Information and Communication Technologies (ICTs) support job searches, resume creation and the ability to highlight employment skills on social media. However, these technological tools are often tailored to high-income, highly educated users, and white-collar professionals. It is unclear what interventions address the needs of job seekers who have limited resources, education, or who may be underserved in other ways. We gathered insights from past literature and generated ten tangible design concepts to address the needs of underserved job seekers. We then conducted a needs validation and speed dating study to understand which concepts were most viable among our population. We found that the three most preferred concepts immediately addressed job seekers’ most practical needs.

  • Per reviewer feedback, we aim to improve the utility of this publication to other scholars by including our research materials here. This dataset includes the interview script, storyboards that were used in the needs validation study, the demographics survey/questionnaire, and the consent form.
Creator
Depositor
  • tdillahu@umich.edu
Contact information
Discipline
Funding agency
  • National Science Foundation (NSF)
ORSP grant number
  • IIS-1717186
Keyword
Resource type
Last modified
  • 11/05/2019
Published
  • 03/23/2018
Language
DOI
  • https://doi.org/10.7302/Z2BV7DSF
License
To Cite this Work:
Dillahunt, T. R., Lam, J., Lu, A., Wheeler, E. (2018). Designing Future Employment Applications for Underserved Job Seekers: A Speed Dating Study [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/Z2BV7DSF

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