JobDESIGNER
A talent management tool that simplifies
job analysis, recruitment, and training
A talent management tool that simplifies job analysis, recruitment, and training
JobDESIGNER: Job Data Extraction, Selection, and Input through Gathering Non-Expert Responses
Talent management is cumbersome. To hire the right people and identify training needs and promotion pipelines, organizations need a clear understanding of job requirements. In large organizations like the Army, it takes significant effort to determine exactly what people do in each role. Traditional methods involve job analysis experts conducting multi-part, lengthy surveys and interviews with professionals who have deep knowledge of the job.
The JobDESIGNER tool uses crowdsourcing methods to simplify the process of updating job requirements and tasks, so even non-experts can conduct needed surveys and updates frequently.
“You have to establish all the duties, tasks, knowledge, skills, abilities and other characteristics (KSAOs) of each and every job, and it’s a time-intensive process.”
Leonard Eusebi
Senior Scientist and Principal Investigator on JobDESIGNER
Employing job analysis experts and professionals familiar with the field and job requirements for this invasive and time-intensive work is expensive. As a result, the surveys are not conducted often. But infrequent updates are a challenge, especially in the face of rapidly changing technology. The skills needed tomorrow might look very different from what’s needed today.
JobDESIGNER, which stands for Job Data Extraction, Selection, and Input by Gathering Non-Expert Responses, addresses these challenges by significantly reducing the role of the expert. The goal is to create a system that is informed by job analysis experts but executed and updated by non-experts. The team, which included PSI Services, Inc., worked with a preexisting collaborative web-based platform to distribute short-workflow surveys to multiple professionals in the field, or stakeholders. Crowdsourcing spreads out the commitment required and eases the burden on any single job stakeholder (incumbent, peer, trainer, or supervisor). Individuals can address a select few survey points while the team, as a collective, paints a complete picture portion by portion.
Using JobDESIGNER requires less effort from many people and allows for frequent updates of current and evolving tasks, along with the KSAOs required for each. It functions as a probabilistic model of the job, drawing on a wide range of responses from different people that overlap to create a clear picture.
To cut down the work required by job analysis experts and job stakeholders, JobDESIGNER delivers dynamic automated surveys that intelligently select a subset of duties, tasks, or skills that may be relevant to each job. While preexisting taxonomies for job descriptions, the set of tasks needed for good customer service, for example, provide an initial model, updating the specifics of tasks is more challenging. After all, customer service for a bus driver will look different from customer service requirements for a restaurant worker.
The frequency of updates and the number of people surveyed can change over time depending on needs. Eusebi expects commercial applications of JobDESIGNER in recruiting, where the data will help human resources personnel tailor job postings to exactly what they need.
“JobDESIGNER significantly reduces the individual burden of a job analyst and allows for continual updates and understanding of the requirements of a job,” Eusebi says. “It allows for better tailoring of training and recruitment targeting.” Equally important, it enables recruitment and talent management to keep pace with rapidly changing technology.
Contact us to learn more about JobDESIGNER and our other adaptive intelligent training capabilities.
This material is based upon work supported by the Army Research Institute for the Behavioral and Social Sciences and the Army Contracting Command under Contract No. W911NF-20-C-0024. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Army Research Institute for the Behavioral and Social Sciences or the Army Contracting Command.