A University of La Verne dissertation used Evidano to analyze how engineering managers retain talent in manufacturing

Voluntary turnover among engineers is costly for manufacturers, yet research on retention specifically within manufacturing engineering is limited. This qualitative case study asked what retention strategies engineering managers actually use.

The analysis drew on Herzberg’s two-factor theory, which separates factors that prevent dissatisfaction, such as pay, working conditions, and policy, from those that motivate people to stay, such as growth, recognition, and meaningful work, and on transformational leadership theory. The dissertation was completed in the LaFetra College of Education.

Engineering managers were purposefully sampled and took part in semi-structured interviews, supplemented by document review.

The data were coded and analyzed using thematic analysis with the support of Evidano, giving the researcher a structured way to compare managers’ accounts against the two theoretical frameworks.

Retention emerged as multifaceted rather than driven by a single lever. Engineers were described as more likely to stay when they were engaged in their work, when leadership communicated clearly, and when growth opportunities were visible.

The dissertation recommends that manufacturers formalize growth and advancement paths, build a more transparent culture, and train managers in leadership. It suggests future research in other sectors and from engineers’ own perspective rather than managers’.

This case shows Evidano supporting an applied doctoral study in workforce and organizational research, where interview data must be read against established motivation and leadership theory.

For HR leaders and engineering managers, it offers a research-based view of retention that goes beyond pay to the day-to-day practices managers control.

The study captures managers’ perspectives rather than engineers’ own reasons for staying or leaving, and the findings come from a qualitative case study in one sector.

This summary is based on the dissertation’s public abstract and record; the full text is available only to University of La Verne users, so details such as sample size and the exact division of work between Evidano and the researcher are not described here.