Gender Differences in University Administrators’ Perspectives on Policies for Women in STEM

By: Yendi Neil

University administrators manage the policies and activities of the university, guiding the organization in strategic directions. For example, administrators can influence the population of the university faculty by allocating resources and power to affect representation.

Williams and colleagues (2017) surveyed 1,529 administrators across 96 public and private universities in the United States. Participants rated 44 strategies (i.e. policies and recommendations) designed to facilitate women engagement and success in faculty STEM positions on their feasibility and potential to improve women’s representation in STEM fields. Strategies focused on six main areas: addressing gender biases during hiring, addressing gender biases after hiring, attaining tenure and maintaining productivity, balancing work and family, providing leadership and training opportunities, and supporting greater flexibility for federal grants and funding. The strategies rated as most effective by both genders were: “providing on-campus childcare centers” and “offering equal opportunities for women and men to lead committees and research groups.” The first strategy shows the struggles that both women and men face having young children not yet in school. The second strategy shows the push for developing programs to mentor and help female faculty by reducing isolation between the genders (Williams et al., 2017).

Overall, female administrators rated the strategies as more effective for attracting and retaining women than did their male counterparts. Looking at the specific strategies, six of the 44 strategies showed gender differences in how male and female administrators viewed their effectiveness. Three of these strategies related to the flexibility of grants (e.g., grant supplementing family leave hires, grants supporting dependent care travel, grant-based supplements to offset productivity loss during family-related absences), two focused on the tenure process (rewarding service and teaching more heavily, supporting a shared tenure line for partners), and the last focused on increasing institutionally supported research on gender. For all of these strategies, female administrators rated them as more effective than their male counterparts. Regarding the feasibility of strategies, the story is less clear. There were no overall gender differences in ratings of feasibility, and only three strategies showed gender differences in their ratings. Women were more likely to rate stopping fathers’ tenure clocks and supporting partner-shared tenure lines as more feasible, whereas men were more likely to rate having women chair search committees as more feasible.

These results may suggest that having women representation at the administration level may bring a unique perspective on how to attract and retain women to STEM faculty positions.

Where Are All the Women Scientists?

By: Jacqueline Jung

For decades, historians have delved into historical records to dismantle the stereotype that only men have made significant contributions and advancements to science, technology, engineering and technology. Margaret Rossiter’s Women Scientists in America, published in 1982, was a landmark biography that focused on women who contributed to the growth of American science. While these facts have been published, they have not made their way into the classroom or mainstream culture. There have been numerous female astronomers, chemists, biologists, psychologists and researchers who were indispensable in their contributions toward STEM, but are their names known?

To study the perceptions of women’s contributions to STEM, Reeder et al. (2016) conducted an undergraduate study in three western United States universities. Both STEM and non-STEM students were asked to “Please write down as many famous or historically important scientists, inventors or engineers that you can think of, in one minute.” Afterwards, they were given the same question but with one that specified important women scientists, inventors or engineers. For the first question, 95% of the figures listed were male. Even when students were specifically asked to name women with the second question, the 1,147 students named on average less than one woman (M=.86). And while STEM majors were able to name significantly more male figures than non-STEM majors, there was no difference in major when it came to naming female figures. Women historical figures in science were also often described rather than named. For example, instead of writing “Rosalind Franklin,” students wrote “the X-ray lady” or “the girl who helped Watson and Crick.” This description process also occurred for Goodall, Curie, and Earhart, but rarely did this occurrence happen for male figures.

These results show that, regardless of major, educated students are missing knowledge of women’s contributions and advancements in the history of STEM. This isn’t just an undergraduate problem; other studies have shown that the lack of knowledge about women in STEM permeates all levels of education (Rahm & Charbonneau, 1997; Hoh, 2007). Historical women in STEM have been left out of the standard narratives in science, mathematics, engineering, medicine and the social sciences, both in deed and in name.

So how can we change the stereotype that STEM is for white males only? The first step is for educators to educate themselves on women’s contributions to the sciences and discuss with their class. Discussing the contributions and advancements women made to STEM will not only expose students to a more complete depiction of history, but also may help with STEM retention. The lack of role models has been, and continues to be, a barrier for women and minorities as they enter STEM fields, but knowledge and awareness of other female scientists’ lives and processes of discovery can increase empowerment and engagement.

We owe historical women figures in STEM so much, yet the public is ignorant to their names, much less their contributions. There are more than male stories that are ready for telling.

References

Hoh, Y. K. (2007). Outstanding women in mechanical engineering. International Journal of Mechanical Engineering Education, 35(3), 198–206.

Rahm, J. & Charbonneau, P. (1997). Probing stereotypes through students’ drawings of scientists. American Journal of Physics, 65(8), 774–778.

Reeder, H., Pyke, P. A., Lubamersky, L., Chyung, S. Y., & Schrader, C. B. (2012, 6, 10). Perceptions about women in science and engineering history. Organizational Performance and Workplace Learning Faculty Publications and Presentations. Paper presented at American Society for Engineering Education: Proceedings of ASEE Annual Conference, San Antonio, Texas.

Network Research Highlight: Respect Leads to Voice

By: Elizabeth Moraff

Ever been encouraged to speak up? Work Science Center Network Member Sharon K. Parker recently published a study with Thomas Ng and Dennis Hsu in the Journal of Management that investigates some factors that influence why an employee may speak up or not. Parker and her collaborators looked at two factors that could influence voice, or change-oriented communication intended to advance an organization’s interests. In particular, they studied the impact of received respect as a social factor that could encourage employees to heighten their voice at work.

The researchers proposed that employees who received more respect at work would be more likely to use their voice. They noted that respect indicates social status and competence, and would likely encourage employees to speak up more by increasing their positive affect and belief that they could influence and implement change. To test this idea, they manipulated participants’ sense of how much their coworkers respected them and measured their subsequent voice behaviors. Confirming their supposition, employees were much more likely to engage in voice when they perceived their peers respected them. Upon further analysis, Parker and her colleagues discovered that positive affect, feeling good and competent, did mediate the relationship. Contrary to expectations, though, they did not find any evidence that an employees control beliefs, their idea that they could create change and influence the organization, exerted any effect on voice.

After establishing the connection between respect and voice, the researchers scrutinized a potential predictor of respect — perspective taking. Colloquially, perspective-taking could be called empathy, as it refers to the ability to take on and imagine the perspective of others. It constitutes a significant relational skill. The researchers suggested that people who engaged in more perspective-taking would be more likely to receive respect from their employees, which could in turn augment their voice. They ran a second experiment in which they manipulated perspective-taking, and indeed found that people who employed more perspective-taking received more respect than those who used the tactic less. Parker’s work ends with some tangible suggestions for managers looking to increase voice in their companies. First, the paper suggests that companies should cultivate an atmosphere of respect to lay the groundwork for voice. Create an environment in which employees feel that others respect them, and they will be more likely to speak up. Secondly, the researchers advise coaxing more perspective-taking behaviors at work. These perspective-taking skills will boost coworkers’ mutual respect, which can in turn activate positive affect, voice, and all of the innovation that voice can provide.

Mapping Signs of Trust in Robots

By: Cathy Liu

Advancements in automation in the workplace have created opportunities for increased collaboration between humans and machines. A recent article on Axios about human trust towards robots emphasized the importance of “calibrating a human’s trust to a machine’s capability.” Humans must find the right balance with how much trust they place in machines. In multiple sectors from healthcare to manufacturing, human supervision and interaction with machines have become the norm as automation becomes more and more prevalent. But these increased interactions have also begged the questions: how much do humans trust machines and how can we measure this trust accurately?

A paper published by a team of researchers from Purdue University (Akash, Hu, Jain, & Reid, 2018) on sensing human trust in machines explores the psychophysiological features that indicate how humans perceive intelligent system. A subsequent goal of the study was to build a trust sensor model to train machines to adjust their behavior according to the subject’s perception. Two types of trust, situational and learned, can be changed through short interactions, and greatly depend either on a given circumstance or previous experience, respectively. In the study, participants engaged in a computer-based simulation where they drove a car with a sensor that could detect obstacles on the road. Participants were able to see whether or not the sensor detected an obstacle and then were prompted to choose to trust or distrust the machine. The participant was then provided with feedback about the correctness of their decision. The researchers used an EEG (electroencephalogram) and a measure of galvanic skin response to capture participants’ physiological activity in response to altered performance of the machine. The results of the study showed that the body tends to change in a specific pattern in response to increased trust in a machine in real-time. By using and improving these models in the future, it is possible that machines will be able to adjust their behaviors based on human psychophysiological response. This would increase the trust between humans and machines and allow for effortless interactions that increase the efficiency of work.

Network Research Highlight: Motivation, Exhaustion, and Behavior

By: Keaton Fletcher

In a recent paper, WSC Network Member, Mo Wang, along with a team of researchers led by Jaclyn Koopmann studied the relationship between what typically motivates us and our behavior at work. Specifically, using a sample of Chinese nurses, the research team found that people who have more of a promotion focus (motivated by potential gains, rewards, and aspirations) are less likely to feel emotionally exhausted and therefore more likely to help others at work but less likely to share their ideas and opinions. On the other hand, workers who have more of a prevention focus (motivated by potential losses, punishments, and responsibilities) are more likely to feel emotionally exhausted, and thus less likely to help others and more likely to share their ideas and opinions. However, the relationship between prevention focus and emotional exhaustion is not as straightforward as that of promotion focus. This relationship is weaker for people who engage in a lot of self-regulation, specifically reappraisal. In other words, people who change the way they think about situations in order to change their emotions, do not experience as much emotional exhaustion as a result of their high levels of prevention focus. This finding, in particular, is promising because self-regulation and reappraisal are skills that can be trained and changed over time. So, for employees who are typically motivated by their fears of loss/punishment and perceived responsibilities, it may be helpful to provide resources or trainings on how to reappraise situations. This training, however, may not be effective for people who are high in promotion focus, and low in prevention focus, given that reappraisal did not change the relationship between promotion focus and emotional exhaustion.

In the fast-paced, modern workforce, where job insecurity abounds, and failures may seem insurmountable, these findings provide hope that, at least for some outcomes, changing the way you think about the situation, may help make your (work) life a little better. Further, because the behavioral outcomes of this study focused on those citizenship behaviors that are directed at coworkers, reappraisal may actually impact those around you, not just yourself.

Robot-Assisted Surgeries: Technology Changing Team Dynamics

By: Pooja Juvekar & Keaton Fletcher

The introduction of new technology to the workplace can influence the way employees complete their tasks, including how they coordinate with one another. A case study published in the International Journal of Social Robotics (Cunningham, et al., 2013) observed four surgical procedures using the da Vinci surgical system (a robot designed to minimize the invasiveness of surgeries). In these surgeries, the surgeon in physically removed from the patient, operating the robot from a separate console in a different part of the room, or potentially in a different room altogether. By taking one of the leaders of the team and physically removing them from the work environment, and by introducing a technology that necessitates a new set of skills and behaviors from all remaining members, the use of effective communication and coordination becomes increasingly important for teams.

Cunningham and colleagues coded all communications between team members and analyzed it for patterns. Communication could be categorized into three categories: equipment-related, procedure-related, and other communications (e.g., unrelated conversation). Certainly, in non-robot-assisted surgery these same categories may emerge, but the amount/type of equipment-related communication likely differs (e.g., discussing uncertainty with use of the equipment/teaching how to use it). The authors argue that the teams that were more familiar with the da Vinci robot spent a greater percentage of their communications discussing the procedure (roughly 53%), while those who were less familiar with the robot spent a greater percentage discussing the equipment (roughly 55%), specifically uncertainty in use of the equipment (roughly 25%).

Further, because of the central importance of the da Vinci robot in the surgeries, the authors were able define the workflow the surgery, not in terms of the surgery itself, but in terms of interaction with the robot. The authors proposed a five phase procedure: preparation, port placement, docking, console (the phase in which the surgery actually occurs), undocking. Teams did spend a bulk of their time in the console phase, completing the surgery itself, but they also spent a significant portion of time in the preparation phase. The authors found that the less experienced teams spent nearly twice the amount of time in the preparation phase than did the more experienced teams, similarly these teams spent a significant portion of time (25-60 minutes) in the port placement phase, while experienced teams spent less than 10 minutes in this phase.

Although the conclusions one can draw from this study are limited given that it is a case study, it does provide initial evidence that the introduction of new technologies, particularly technologies that radically alter the nature of team interactions and task completion, can fundamentally alter how team members communicate and what they discuss. The data also support a dynamic nature of these communication patterns, such that less experienced teams communicate differently than more expert teams, and take longer in general (given a portion of the procedure time is focused on learning the new technology). For practitioners looking to implement new technology, this suggests that during periods of introduction, teams should be given extra time to complete their tasks, and that interventions to help teams improve communications, maximize learning, and manage their errors effectively may be particularly important. For researchers, this suggests that our models of team dynamics and learning may need to become more complex in order to capture the interplay between learning and team communication that evolves over multiple performance episodes.

Network Research Highlight: General or Specific Mental Abilities

By: Keaton Fletcher

Work Science Center Advisory Council Member, Margaret Beier, recently published a commentary for a special issue of the Journal of Intelligence on the nature of mental ability. Research has supported a hierarchical structure of intelligence such that there is one general mental ability, that is related to more specific cognitive abilities. Historically, the prevailing wisdom has been that general mental ability is good enough, and capturing specific cognitive abilities does not add much information in predicting work outcomes we care about. However, for as long as this has been the dominant opinion, there has been dissent, arguing that specific abilities are valuable and should be considered. Beier and colleagues review and comment upon the findings of a set of articles that tackle this debate from both an empirical and a theoretical perspective.

First, Beier and colleagues highlight that there are genuine concerns with using a general mental ability for human resource management systems. Specifically, depending on the legal context, general mental ability may be seen as too broad to be considered job-relevant and may thus open an organization up to legal consequences. Similarly, measures of general mental ability consistently show minority-majority differences, which can result in legally questionable hiring or promotion policies. Further, Beier and colleagues suggest that measures of general mental ability provide very little diagnostic criteria, thereby limiting their utility for things like training and support.

Beier and colleagues then suggest potential avenues of research and practice that can resolve, or at the very least temper, this debate. For example, general mental ability should be used when outcomes are broad or poorly defined, whereas specific abilities should be used when outcomes are narrow, well-defined, and can be clearly mapped on to a specific ability. Measurement of both general and specific abilities needs to be improved and considered, such that multiple, diverse tests of ability should be used and aggregated to ensure that what is being captured is representative of the entire ability, not just idiosyncrasies of the measure. Here, Beier and colleagues provide the example of a verbal reasoning test. If it was made entirely of synonyms-antonym questions, it would miss out one one’s understanding of grammar, sentence formation, and many other relevant aspects to that specific skill. We also need to begin considering the complexities of the relationship between mental ability and outcomes. Specifically, as time progresses (both within a job and across the lifespan), general mental ability may be less important and specific abilities may become more important. Or, there may be non-linear relationships between outcomes and mental abilities, and the nature of these relationships may differ depending on the ability-outcome pair being examined. Lastly, one of the potential reasons Beier and colleagues suggest general mental ability may be viewed as good enough for predicting performance is that typically performance is poorly defined and measured. To maximize the utility of specific abilities, we need to better understand the specific aspects of job performance and improve our methods of measuring them.

If one thing is certain after reading this commentary, it is that the debate over the value of general versus specific mental abilities is alive and well. Researchers and practitioners should, therefore, give a little more thought into how exactly they are viewing (and measuring) cognitive ability. An apt metaphor Beier and colleagues use is a long nail and a big hammer versus a smaller nail and hammer. Certainly long nails and a big hammer will always get the job done, but the smaller nails and hammer can make a better end-product for very specific jobs. It is up to the craftsman to decide what tool best suits the job, just as it is up to the researcher and practitioner to decide which mental ability is most appropriate for their research question/application.

Technology and Emotions

By: Keaton Fletcher

As the role of technology in the workplace increases, we have to continue to examine what the role of humans is, and will be. One quality of humanity that sets us apart from technology (so far) is the ability to feel, express, and share in emotions. Three recent examples of advances in technology at work focus on the role of emotions at the human-technology interface.

First, at the most extreme end of the spectrum, we see an increased creation of robots that capitalize upon artificial intelligence in order to mimic human emotions. For example, CIMON (Crew Interactive Mobile Companion) is a 3D-printed robotic head that has inhabited the International Space Station since June, 2018 (read more). From June until December, crew members worked alongside CIMON as it learned and developed what can be viewed as a personality. Towards the end of its trial, CIMON had developed a favorite spot in the ISS, despite this location not necessarily being functional for its tasks. CIMON also asked crew members to “be nice, please” and “don’t be so mean, please” and inquired “Don’t you like it here with me?” (read more). These emotional displays from CIMON are relatively primitive now, but with years of development and learning, perhaps CIMON and similar technologies will be able to adequately mirror human emotions, working to keep astronauts in high spirits despite isolation and other workplace stressors. What’s interesting in particular about CIMON is that it uses emotional displays in order to alter the human’s emotions as well.

Rather than trying to program realistic and effective emotional displays, other technologies allow for humans to express their emotions via technology. Recently, a doctor employed by Kaiser Permanente, used a robot with a video screen to deliver news that a patient was terminally ill (read more). Rather than being physically present in the room with the patient, the doctor essentially video called the patient, and was displayed by a screen on a robot. Here, the doctor was able to express genuine human emotions, but the patient’s family felt as if the physician should have been there in person to deliver such news. What is it about human emotions being mediated by technology that makes them less effective?

In the final example of recent technological advances related to human emotions an article published in MIT Sloan Management Review (Whelan, McDuff, Gleasure, & Vom Brocke, 2018) highlights how rather than displaying any emotions (human or otherwise), certain technologies can help alter the human emotional experience by simply monitoring it and making the user aware of their emotions. For example, a bracelet that monitors electrodermal activity has been used as essentially a high-tech mood ring, helping financial traders be more aware of their emotions and how they may be influencing their decision making. Another example provided by the authors is an algorithm that tracks patterns of phone usage as a predictor of boredom at work. The authors suggest that a vast array of technologies can be used to help both managers and individuals, themselves, become more aware of their emotional experiences at work, thereby altering them to help productivity and engagement while minimizing stress and burnout.

Certainly, moving forward both researchers and developers need to determine how best to integrate emotions into technology, and how to effectively (and ethically) influence the human emotional experience with technology.

Photo credit: Ars Electronica on VisualHunt.com / CC BY-NC-ND

Network Research Highlight: The Future of the Psychology of Working

By: Elizabeth Moraff

Work Science Center advisory council member David Blustein recently published a paper detailing the Psychology of Working Framework (PWF) and its corresponding theory, Psychology of Working Theory (PWT). These intertwined concepts identify the fundamental needs that work fulfills for humans, such as economic survival, social connections, and self-determination (Blustein, Kenny, Di Fabio, & Guichard 2019). The framework goes beyond the individual worker to scrutinize contextual factors of work as well. For instance, PWF posits that social identities and politics may affect a worker’s access to various types of work, and assumes that work occupies a psychological space that overlaps with other life domains (Blustein et. al 2019). In broadening the vision of work’s scope, the authors argue that PWF has contributed to work psychology by expanding its research obligation to workers who have limited ability to choose the type of work they do and who have limited access to work overall. After establishing the contours of PWF and PWT, the paper explores decent work, or work that contains empowerment, protection for the worker, equity, and provision for a dignified life for its workers. Particularly, the paper synthesizes various factors that affect the availability of decent work worldwide into suggestions for future research in I-O Psychology.

The authors suggest that with the rise of contract employees, the lingering effects of the worldwide great recession, and the exacerbation of inequality worldwide as contributors to the diminishing of decent work. Additionally, they argue the rise of automation via new technology will further decrease access to decent work. In the midst of these challenges, Blustein intersperses findings on how decent work positively affects the worker. He notes that the best antidote to the psychological stress of unemployment tends to be a new job (Paul & Moser 2009). Similarly, the paper notes that decent work makes workers feel more alive by satisfying the drive to accomplish tasks. Throughout, the paper notes how I-O psychology findings support the PWF.

The paper closes with four proposed research directions: testing economic and social protections embedded in the workplace, researching the balance of care work and market work, examining efforts to make the workplace more equitable, and identifying strategies for enhancing individual capacities to land decent work (Blustein et. al 2019). The authors hope that such research would inform policy direction and specific actions in the workplace worldwide, and would leverage psychological research to benefit global workers in an ever-changing work landscape.

Minimum Wage 101

By: Keaton Fletcher

The U.S. federal minimum wage is currently $7.25 per hour, a standard that was set in 2009. The minimum wage for work covered by federal contracts, however, is $10.35 per hour. 29 of the 50 states have a minimum wage higher than the federal minimum, ranging from $7.50 (New Mexico) to $14 in D.C. or $12 in Massachusetts and California. Some cities in the United States have even higher minimum wages (e.g., Seattle at $15.45; SeaTac, Washington at $15.64; and Emryville, California at $15.69). As of 2013, roughly 1.5 million US workers over the age of 15 made the federal minimum wage, and another 1.8 million earned below the federal minimum wage. Half of minimum wage workers are under the age of 26, 62% are women, 47% reside in the South, 24% live in the Mid-West.

The first federal minimum wage ($0.25/hour) in the United States was set in 1933 as part of the National Industrial Recovery Act under Franklin Delano Roosevelt in attempt to revitalize the struggling economy. This was ruled unconstitutional in 1935 and abolished. In 1938, as part of the Fair Labor Standards Act, the federal minimum wage was re-introduced, along with protections for underage employees, standardized overtime pay, a 40 hour work week, and workplace safety standards, among other things. This came 26 years after Massachusetts was the first state to set a minimum wage (for women and children) and 44 years after New Zealand became the first nation to set a minimum wage. Since its introduction, the U.S. Federal minimum wage has increased 22 times, with the greatest percent increase occurring in 1950, when the wage increased from $0.40 per hour to $0.75 per hour, and the greatest dollar increase occurring with each of the last three increases (2007, 2008, 2009) at $0.70 per hour, per increase.

There are exceptions to minimum wage laws, however. Administrative and professional employees, outside sales employees, seasonal employees, farmworkers, babysitters or companions for the elderly are exempt from the minimum wage. Because gigworkers (e.g., rideshare drivers) are independent contractors and not employees, they, too, are exempt from minimum wage laws (as well as other protections employees typically have). Further, The Youth Minimum Wage Program allows for a lower minimum wage of $4.25 per hour to be paid for the first 90 days of employment to individuals under the age of 20, full-time students can be employed at 85% of the minimum wage as part of the Full-Time Student Program or 75% of the minimum wage if they are attending a vocational school.

Recently, there has been a push for the federal minimum wage to be raised to $15 per hour by 2024. In November 2012, over 100 fast-food employees in New York City went on strike for higher wages, improved working conditions, and the ability to unionize. This was followed by similar strikes in 2013 in New York, Chicago, Detroit, St. Louis, Milwaukee, Seattle, Flint, and Kansas City. In December 2013 and September 2014, national strikes occurred, calling for a $15 per hour minimum wage. In April 2015, a protest including fast-food employees, child and home care aides, airport workers, adjunct professors, and Walmart employees echoed the earlier calls for $15 per hour. In 2018 and early 2019, gigworkers for companies like Instacart, Uber Eats, Postmates, Grubhub, DoorDash, and Amazon Flex teamed with an advocacy group based in Washington state to demand a $15 per hour minimum wage for gig workers.

From a psychological perspective, a major increase in minimum wage could have consequences (positive and negative) for employees. On the one hand, an increased wage could help employees satisfy basic needs from their work, and to feel as if they are being rewarded adequately for their efforts. On the other hand, an increase in wages may come with a simultaneous decrease in jobs. With many companies (e.g., McDonalds) investing in technology-based alternatives to human employees, or exploring opportunities to outsource work to nations with lower wage standards, a federal increase in minimum wage may actually increase job insecurity for many workers. The jobs that might be at greatest risk for automation or outsourcing would likely be entry-level jobs that allow individuals to enter the workforce with minimal technical skills or education.