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

这项研究的目标是了解需求，并开始建立下一代信息技术工具和应用程序，以满足美国求职者的独特需求，他们生活在低社会经济地区，教育程度有限，或收入低。今天的许多技术都是为了满足相对富裕人口的需要，而很少考虑到得不到充分服务的人口的需要。这就导致了线下的机会不平等转移到了线上的求职方式。因此，得不到充分服务的求职者可能缺乏信心、技能和经济手段，无法利用用于支持就业过程的主流技术。他们面临的障碍，如阐明工作技能和使用这些技能来开发简历，开发教育途径，以获得所需的工作技能，并给予成功的面试。随着非技术雇主和公司越来越多地使用主流在线招聘和面试工具，数字招聘鸿沟可能会扩大并加剧服务不足人群的就业困境，除非像本项目这样的研究找到缓解问题的方法，并使信息技术适应这一重要部分劳动力的需求。 该研究将填补在服务不足人群中使用数字招聘和就业工具的挑战研究的空白，并最终导致更好的数字就业和招聘软件。该项目将应用计划行为理论作为评估数字就业应用的视角和指南。研究结果将扩展该理论，以包括服务不足的求职者和利益相关者（如支持他们的就业中心的经理和工作人员）所面临的数字障碍和限制。众所周知的人机交互方法将用于迭代构建和增强三种替代数字就业和招聘应用程序，以评估其对求职态度，主观规范（或社会支持）和感知行为控制（或自我效能）的影响-所有可能导致工作成就的因素。这些应用包括：（1）SkillsExtractor，一个概念验证原型，使用开放技能项目提供的开放数据集，从过去的职位搜索中提取和识别工作技能;（2）Interview4，一个现有的非主流工具，使求职者能够进行模拟面试，并将这些面试发送给朋友以获得反馈;以及（3）Review-Me，这是一个试验性应用程序，研究团队构建、部署和评估了它，通过将求职者与志愿者联系起来进行简历审查来争取社会支持。该项目最后将努力评估这些工具，并确定和报告将受益于政策干预的挑战。这项研究将提供重要的理论见解，了解哪些技术功能在服务不足的人群中有效和无效，以及为什么。

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.