TY - JOUR
T1 - Objectivity by design
T2 - The impact of AI-driven approach on employees' soft skills evaluation
AU - Gafni, Ruti
AU - Aviv, Itzhak
AU - Kantsepolsky, Boris
AU - Sherman, Sofia
AU - Rika, Havana
AU - Itzkovich, Yariv
AU - Barger, Artem
N1 - Publisher Copyright:
© 2024
PY - 2024/6
Y1 - 2024/6
N2 - Engineers’ team collaboration skills are among software development's most important success factors. Existing Artificial Intelligence practices for the engineers' soft skills assessment mainly rely on evaluations of subjective data gathered through surveys, interviews, or observations. As a result, the insights gained by these methods are biased because of the subjective data people report. To overcome the challenge of subjectivity, we offer a novel objectivity-by-design approach for continuous AI-driven team collaboration skills analytics. The method analyzes the data from workstreams gathered from data repositories like Jira. Based on the study results, we conclude that this approach enables a continuous assessment of employees' team collaboration skills, provides more accurate insights, eliminates subjective biases, and helps uncover trends and deficits on individual and team levels. Understanding and recognizing employees' strengths and weaknesses can foster an organizational culture of growth and development. An improved organizational climate is expected to result in work satisfaction, engagement, and motivation, thus positively impacting employees, businesses, and society.
AB - Engineers’ team collaboration skills are among software development's most important success factors. Existing Artificial Intelligence practices for the engineers' soft skills assessment mainly rely on evaluations of subjective data gathered through surveys, interviews, or observations. As a result, the insights gained by these methods are biased because of the subjective data people report. To overcome the challenge of subjectivity, we offer a novel objectivity-by-design approach for continuous AI-driven team collaboration skills analytics. The method analyzes the data from workstreams gathered from data repositories like Jira. Based on the study results, we conclude that this approach enables a continuous assessment of employees' team collaboration skills, provides more accurate insights, eliminates subjective biases, and helps uncover trends and deficits on individual and team levels. Understanding and recognizing employees' strengths and weaknesses can foster an organizational culture of growth and development. An improved organizational climate is expected to result in work satisfaction, engagement, and motivation, thus positively impacting employees, businesses, and society.
KW - Artificial intelligence
KW - Data Science
KW - Employees analytics
KW - Machine learning
KW - Soft skills assessment
UR - https://www.scopus.com/pages/publications/85187222029
U2 - 10.1016/j.infsof.2024.107430
DO - 10.1016/j.infsof.2024.107430
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AN - SCOPUS:85187222029
SN - 0950-5849
VL - 170
JO - Information and Software Technology
JF - Information and Software Technology
M1 - 107430
ER -