Scientists and engineers (S&Es;) are fundamental pillars of technical organizations. Managing the intellectual human capital is challenging and critical to organizational success. The literature that deals with the management of S&Es; remains grounded in theory developed by studies made in the 1950s and 1960s. Although the context and characteristics of this workforce have changed over the years, deeply embedded assumptions and broad generalizations about S&Es; remain the same in modern literature. There is a need to revisit and update the underlying assumptions about technical professionals through deep empirical work in order to keep management of technical organizations connected to the reality of today’s workforce. From a practical perspective, managers need to understand their employees’ motivations to be able to properly incentivize them. This research aims to answer the following questions: What motivates scientists and engineers today? How do scientists and engineers respond to different incentives? How can this knowledge be used to improve incentives for scientists and engineers? We take two approaches to answer these questions. First, we quantitatively test old assumptions about motivations of S&Es; in a large and current data set. We found that there is no strong support for clear-cut distinctions between scientists and engineers; this is not to say that there are not meaningful differences in categories of employees just that those differences do not fit cleanly along scientist and engineer lines. Also, we found that commonly used measures on motivation and job satisfaction have limited usefulness to managers trying to create effective incentives for their technical personnel. To improve the situation, we use an inductive approach to develop better measures, qualitatively exploring what motivates S&Es; and how they react to different incentives. We found that S&Es; have a variety of motivations for work that can be grouped in three dimensions: social, temporal, and technological. Individuals’ preferences within each dimension influence the way they react to incentives. For example, a scientist or engineer with a social orientation will favorably react to incentives that involve interaction with others such as taking on management roles. Our results call for more attention to the variety of orientations within the workforce as a way to improve the management of scientists and engineers in today’s technical organizations.
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