Computing Professionals in Industry and Academia View a Career in Computing Differently
Individuals’ beliefs about what a particular career path would allow them to do is a major determinant in whether they will pursue that path. This graphic shows perceptions of computing professionals in the industry and academia of how computing careers would allow them to serve humanity, be in a position of influence in society, and spend a lot of time with family.
Results indicate that professionals in academia believe more strongly than those in industry that a career in computing would allow them to serve humanity and be in a position of influence in society than do professionals working in industry. On the other hand, professionals in industry believe more strongly than those in academia that a career in computing would allow them to spend time with family.
- The survey data used in this graphic were collected in 2020 survey cycle by CERP via the CRA Data Buddies Project.
- The graphic shows percentage of people in each group who indicated their level of belief in each item as being quite a bit (4) or very much (5) on a scale of not at all (1) – very much (5). All comparisons between the industry and academia percentages were tested using two proportion comparison tests and are statistically significant (p < 0.05).
- Respondents’ employment setting (i.e., industry vs academia) are self-reported and asked only of those who selected indicated that they are not students and are currently employed full/part time. A range of positions and titles are included in each group including postdocs through full professors in academia and entry to senior level positions in industry.
This analysis is brought to you by the CRA’s Center for Evaluating the Research Pipeline (CERP). CERP provides social science research and comparative evaluation for the computing community. Subscribe to the CERP newsletter here. Volunteer for Data Buddies by signing up here.
This material is based upon work supported by the National Science Foundation under grant numbers (CNS-1246649, CNS 1840724, DUE-1431112, and DUE 1821136). 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 National Science Foundation.