Gendered Perceptions of Cultural and Skill Alignment in Technology Companies
“When most people think of the average tech entrepreneur, the pale guy who codes while playing World of Warcraft in his gadget-filled basement pops up.”(Wei 2012)
2. How Stereotypes Affect Perceptions in Tech Fields
2.1. Self-Perceptions of Skill
2.2. Self-Perceptions of Belonging
2.3. Perceptions of Others’ Treatment
2.4. The Current Research
4. Analytical Plan and Measures
4.1. Dependent Variables
4.1.1. Identity Measures
4.1.2. Supervisor Treatment Measures
4.1.3. Turnover Intention Measure
4.2. Independent Variables
4.2.1. Stereotypes about Successful Tech Work
4.2.2. Self-Perception Scales
4.2.3. Cultural and Skill Alignment Measures
4.2.4. Gender and Race Measures
4.2.5. Employee Level
5.1. Summary Statistics
5.2. What Are the Stereotypes About Successful Tech Work?
5.3. How Do Individuals Rate Themselves?
5.4. Are Women Less Likely to Align with the Stereotypes of Successful Tech Work?
5.5. Analytical Strategy
5.6. Is Alignment Associated with Workplace Outcomes?
5.7. Does Career Stage Matter?
6. Summary and Conclusions
Conflicts of Interest
Intensive Work Commitment
|Long Working Hours||0.744||0.111||0.101|
|Company 1||Cultural Alignment||51%||37% **|
|Company 2||Cultural Alignment||58%||36% *|
|Company 3||Cultural Alignment||53%||42%|
|Skill Alignment||71%||32% **|
|Company 4||Cultural Alignment||62%||50%|
|Company 5||Cultural Alignment||57%||57%|
|Company 6||Cultural Alignment||58%||34% ***|
|Company 7||Cultural Alignment||55%||30% ***|
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Research directors at the Anita Borg and Clayman Institutes recruited seven companies to participate in the study. Their recruitment strategy was designed to capture organizational variation within the broad computer and information technology industry and to focus on companies that were known to employ top technical talent. We are unable to name the companies due to promised confidentiality. At the time the survey was completed, software and hardware industry segments were the largest employers in the high-technology sector in Silicon Valley, and these industry segments constitute the company sample. Surveys were administered to employees who comprised the core Silicon Valley technical workforce at each participating company; companies defined their “core technical workforce in the Silicon Valley region” for the researchers. The vast majority of survey respondents identified their field of expertise as software development/engineering and hardware engineering. For more information about the survey methodology, see (Simard et al. 2007).
Some recent research indicates that low response rates are not necessarily associated with significant declines in sample representativeness (Chang and Krosnick 2009; Curtin et al. 2000; Keeter et al. 2000). For example, Chang and Krosnick (2009) found that a sample with a 25% response rate was just as representative as a 43% response rate sample. In addition, response rates have generally declined over time, and the response rates obtained today are considerably lower than those obtainable in 1980, holding budget constant over time (Chang and Krosnick 2009; Holbrook et al. 2003).
Because women are underrepresented in the larger tech industry, an overrepresentation in the sample facilitates analysis by gender. While some might claim sample overrepresentation requires weights, others have argued that sampling weights are not necessary in multivariate analysis if the weight is not a function of the dependent variable, and that weighting in multivariate analysis, at least with the OLS estimator, actually produces inefficient estimates (Winship and Radbill 1994). Thus, we did not include sampling weights in our analysis.
We also ran our analyses with multiple imputation using a multivariate normal model (models available upon request). The patterns of results remain the same. Though some findings change slightly in magnitude and/or in level of significance, our overall arguments remain unchanged. Since very few data are missing, deleting the missing cases does not change our results substantially.
For the “plan to switch career fields” variable described below, there is a “don’t know” answer choice, which we coded as missing.
We used principal-component factor analysis with varimax orthogonal rotations to derive the cultural and skill dimensions. The cultural dimension is a combination of two factors: intensive work commitment and geeky personality. We combined these factors due to their theoretical relevance to cultural perceptions of tech workers. The skill dimension is comprised of one factor. More information is available in the Appendix A.
The scale is constructed by dividing the sum of the question responses by the total number of questions answered. Thus, a value is created for every observation for which there is a response to at least one item (i.e., at least one variable in the scale is not missing). The summative score is divided by the number of items over which the sum is calculated. The scale value thus represents an average.
Because self-ratings are more complicated and nuanced than ratings of successful tech workers, they do not align as neatly with particular “types.” Thus, we prioritized obtaining a good scale (i.e., higher Cronbach’s alpha values) for the ratings of successful tech workers rather than self-ratings. Factor loadings and bivariate Pearson’s correlations of the scale items are available in the Appendix. Breakdowns by gender are available upon request.
We also ran models using the raw difference between individuals’ self-ratings and their ratings of successful tech workers as the dependent variable. The overall patterns are consistent with our direction-magnitude interaction models, but our models provide more specific information. We also ran models using a spline variable. Our direction-magnitude interaction models show how the difference between self-ratings and ratings of successful tech workers affects our outcome variables as the difference gets more negative for the no-alignment group and more positive for the alignment group; spline models show the effect as the difference gets more positive for both groups. Even so, the results of the spline models are largely similar to our models, with the same overall patterns. Models are available upon request.
Some might wonder if men rate themselves higher on all domains than women. It is worth noting in this regard that the gap between men and women’s self-ratings is considerably larger on the cultural domain than on the skill domain. Further, Correll (2001) shows that while men make higher assessments of their mathematical ability, women actually assess their verbal ability higher. This suggests that self-ratings are affected by the gender typing of the domain being considered.
Technically, the effect for those who believe they align equals the interaction coefficient combined with the absolute value coefficient.
While the R2 is low, our main goal is not to explain all variance in our dependent variables. Instead, we are interested in mechanisms that contribute to the gender gap. As research on the effects of stereotypes shows, even small effects can have large impacts as they cumulate over careers (Martell et al. 1996).
It is also possible that the direction-magnitude interaction models (with a dummy variable, absolute value and their interaction) split up the variance in alignment variables so much that it becomes hard to detect independent effects of each component of alignment.
In studies like this, concerns about endogeneity must also be considered. One alternative explanation for our results could be that women perceive a lack of alignment because their supervisors treat them poorly. (In other words, the direction of causality may be reversed.) However, this seems unlikely due to the construction of our alignment variables. Survey respondents were asked to rate the average successful tech worker on a number of attributes, then they rated themselves on those same attributes. Therefore, since we did not directly ask respondents to report perceptions of alignment, but rather constructed the alignment variable from their trait assessments, it seems unlikely that the causal direction could be reversed in this way. While endogeneity can never be ruled out by cross-sectional data, this particular analysis is less susceptible to such concerns due to the way the variables were constructed.
Furthermore, in analyses not shown, we added the identification and perception of supervisor treatment variables as independent variables to the model predicting plans to switch career fields and found that identification with the tech profession, perception that supervisor values opinion, and perception that supervisor assigns high visibility projects all significantly predict plans to switch career fields (p < 0.05). Therefore, by impacting these variables, alignment also indirectly impacts plans to switch career fields in the next 12 months.
|Identify with tech profession a||3.83 |
|Identify with company a||3.40 |
|Supervisor values opinions a||3.91 |
|Supervisor assigns high visibility projects a||3.64 |
|Plan to switch career fields b||1.89 |
|Cultural Alignment (=1)||0.57||0.38 ***|
|Cultural Alignment Absolute Value||0.51 |
|Skill Alignment (=1)||0.66||0.53 ***|
|Skill Alignment Absolute Value||0.58 |
|Other Race||0.06 |
|Low (Entry) Level||0.20 |
|ID with Company||ID with Tech Profession|
|Variables||Model 1||Model 2||Model 3||Model 4||Model 5||Model 6|
|Other Race||0.223 *|
|Cultural Alignment (=1 when self-rating equals or exceeds successful tech rating)||0.134 ***|
|Negative Cultural Self-Assessment, Magnitude (Absolute Value)||−0.216|
|Positive Cultural Self-Assessment, Magnitude (Interaction Term)||0.239 +|
|Skill Alignment (=1 when self-rating equals or exceeds successful tech rating)||−0.053 *|
|Negative Skill Self-Assessment, Magnitude (Absolute Value)||0.083|
|Positive Skill Self-Assessment, Magnitude (Interaction Term)||−0.274 *|
|Supervisor Values Opinion||Assigns High Visibility Projects|
|Variables||Model 1||Model 2||Model 3||Model 4||Model 5||Model 6|
|Female (=1)||−0.133 *|
|Cultural Alignment (=1 when self-rating equals or exceeds successful tech rating)||0.158 *|
|Negative Cultural Self-Assessment, Magnitude (Absolute Value)||−0.305 **|
|Positive Cultural Self-Assessment, Magnitude (Interaction Term)||0.501 **|
|Skill Alignment (=1 when self-rating equals or exceeds successful tech rating)||−0.057|
|Negative Skill Self-Assessment, Magnitude (Absolute Value)||0.087 +|
|Positive Skill Self-Assessment, Magnitude (Interaction Term)||−0.313 ***|
|Variables||Model 1||Model 2||Model 3|
|Cultural Alignment (=1 when self-rating equals or exceeds successful tech rating)||0.033|
|Negative Cultural Self-Assessment, Magnitude (Absolute Value)||0.205 ***|
|Positive Cultural Self-Assessment, Magnitude (Interaction Term)||−0.287*|
|Skill Alignment (=1 when self-rating equals or exceeds successful tech rating)||0.010|
|Negative Skill Self-Assessment, Magnitude (Absolute Value)||−0.086|
|Positive Skill Self-Assessment, Magnitude (Interaction Term)||0.183|
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Wynn, A.T.; Correll, S.J. Gendered Perceptions of Cultural and Skill Alignment in Technology Companies. Soc. Sci. 2017, 6, 45. https://doi.org/10.3390/socsci6020045
Wynn AT, Correll SJ. Gendered Perceptions of Cultural and Skill Alignment in Technology Companies. Social Sciences. 2017; 6(2):45. https://doi.org/10.3390/socsci6020045Chicago/Turabian Style
Wynn, Alison T., and Shelley J. Correll. 2017. "Gendered Perceptions of Cultural and Skill Alignment in Technology Companies" Social Sciences 6, no. 2: 45. https://doi.org/10.3390/socsci6020045