CHS: Small: Designing Next Generation Digital Employment and Recruitment Intervention Tools: Identifying Technical Features to Support Underserved Job Seekers in the U.S.

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The goal of this research is to understand the requirements for and to begin building next-generation information technology tools and applications that address the distinct needs of underserved U.S. job seekers, who live in low-socioeconomic regions, have limited education, or have low income. Many of today's technologies facilitate the needs of relatively affluent populations with very limited consideration of the needs of underserved populations. This leads to offline inequality of opportunity transferring to online means of job application. As a result, underserved job seekers may lack the confidence, skills, and economic means necessary to make use of mainstream technologies used to support the employment process. They face obstacles such as articulating job skills and using these skills to develop resumes, developing educational pathways to gain needed job skills, and giving a successful interview. As non-technical employers and companies increase their use of mainstream online recruitment and interviewing tools, the digital recruitment divide may widen and exacerbate the employment plight of underserved populations, unless research like this project finds ways to mitigate the problems and adapt information technology to serve the needs of this significant part of the workforce. This study will fill a gap in the research on the challenges of using digital recruitment and employment tools among underserved populations and will ultimately lead to better digital employment and recruitment software.This project will apply the Theory of Planned Behavior as a perspective and guide for evaluating digital employment applications. The research results will expand the theory to include digital barriers and constraints faced by underserved job seekers and stakeholders such as managers and staff at job centers who support them. Well-known human-computer interaction methods will be used to iteratively build and enhance three alternative digital employment and recruitment applications to evaluate their impact on job search attitudes, subjective norms (or social support) and perceived behavioral control (or self-efficacy) - all factors that could lead to job attainment. These applications are: (1) SkillsExtractor, a proof-of-concept prototype that uses an open dataset provided by the Open Skills Project to extract and identify job skills from past job title searches; (2) Interview4, an existing non-mainstream tool that enables job seekers to conduct mock interviews and send these interviews to friends for feedback; and (3) Review-Me, a pilot application that the research team built, deployed, and evaluated to enlist social support by connecting job seekers to volunteers for resume review. This project will conclude with an effort to evaluate the tools as well as to identify and report challenges that would benefit from policy intervention. This research will provide important theoretical insights about what technical features are effective and ineffective in underserved populations, and why.