Modern Teams Speaker Series

Date: Friday, February 1, 2019
By: Justin Sabree

The Industrial-Organizational Psychology program at Georgia Tech recently hosted a series of speakers on modern team research in a wide variety of contexts. Below, we briefly outline these talks.

Scott Tannenbaum, President and Co-Founder of the consulting firm, gOE (the Group for Organizational Effectiveness) discussed his experience solving issues for over 500 organizations globally, including more than 30 Fortune 100 companies. Tannenbaum has noticed that almost every organization wants to be better at collaboration and lauds teamwork. This trend has only intensified as the demands to collaborate to solve multi-dimensional problems have increased. However, despite this strong value of teamwork, many workers report that their own teams perform sub-optimally and have misconceptions about what teamwork actually is. Tannenbaum explained that many organizations view teamwork as simply getting along and consequently dissociate it from employee performance and business goals. This dissociation inadvertently downplays the importance of teamwork within an organization. To combat this problem, organizations may consider tying employees’ teamwork ability to their performance assessments and their ability to achieve business objectives.

Before team issues can be solved, organizations need to be sure that the problem truly is team-related. For instance, if a team is underperforming because of general communication issues within that team, then a team-level intervention is appropriate. However, if a team refuses to communicate due to a specific individual, such as a dismissive leader, then the solution should focus on performance management of that individual as opposed to involving the entire team. On the other hand, if many teams fail to communicate effectively, then the problem might lie at a higher level of analysis, and an organizational intervention may be necessary. Ultimately, in order for an intervention to be effective, it must be at the same level of analysis as the problem that it hopes to address.

Greg Ruark, team leader for basic research at the Army Research Institute (ARI), discussed exciting advances in teams research being conducted with the United States Army as well as the future of the field. Ruark explained that, unlike teams in traditional organizations, many teams in the Army serve on two-year rotations, and the individuals on these teams frequently move from different locations. Due to most members being new to a location, team members typically spend a larger amount of their non-work hours together than team members in traditional organization settings. As a result of this reliance on team members outside of work, maintaining positive interactions during non-task related situations is a pressing topic of current research.

Moving forward, Ruark explained that an important frontier for future teams research is how to adapt to new domains of war, such as cyberspace. Traditionally, the Army’s culture has been centralized with a strong top-down influence, but while this set up has been successful for hundreds of years, future attacks may require quicker responses than this design may allow. This may alter the nature of the multiteam system that is the Army.

Lauren Blackwell Landon, Team Risk Portfolio Scientist at NASA, spoke about cutting edge research being conducted with astronauts in preparation for the Mars Mission. This preparation starts with selecting the 12 best candidates out of 18,000 applicants. In a recent job analysis conducted at NASA, the top five needed-at-hire traits for prospective astronauts were sociability, adaptability, motivation, communication, and teamwork. Three of these traits underscore how astronauts must work and live well with others. Due to astronauts cohabitating, group living skills, such as tidiness and sleeping schedule, predict team performance among teams of astronauts much more than would be expected from traditional teams research.

Landon also explained that nuance exists in the desirability of the degree to which astronauts exhibit traits. For example, certain traits, like motivation and emotional stability, have fairly straightforward guidelines: the higher the scores on these traits, the better the team will perform. On the other hand, traits like extraversion seem to be deleterious to team performance if they are present in too high or too low of a quantity. If an astronaut is too reclusive, she may not bond with the team members. However, if an astronaut is too extroverted, he may annoy his fellow team members or fail to function optimally in a relatively isolated environment. Though NASA has a clear view on the ideal range of traits for individuals, Landon mentioned that much less is known about what the ideal composition of traits should be within a team. For instance, the teams literature does not suggest if a team with two introverted members and one extroverted member will perform differently than a team with two extroverts and one introvert.

Though gaps exist about the ideal trait combinations among team members, NASA does focus on other team composition aspects. When composing teams, NASA strategically avoids building teams that have many fault lines, such as gender, profession, or ethnicity. Despite team diversity frequently resulting in positive outcomes, too many surface level fault lines can lead to conflict and subgroup formations.

Steve Fiore, professor at University of Central Florida, spoke about the importance of interdisciplinary science teams and how to study them. Nature’s problems frequently cut across disciplinary borders. Fiore argued that researchers are well-positioned to overcome barriers that have been hindering interdisciplinary scientific research for more than 40 years. When breakdowns occur, the problem should be identified as either relating to issues with task work or teamwork. In regard to scientific teams research, task work is the actual work being completed—hypothesis generation, data analysis, writing up results—and has frequently been the focus of research. In contrast, teamwork involves the attitudinal, behavioral, and cognitive aspects of working with other people. Ultimately, breakdowns in interdisciplinary scientific research can occur due to either type of work, but some common reasons for these breakdowns are from goal misalignment and unclear boundaries.

To overcome these potential degradations in performance, Fiore presented three crucial aspects of teamwork. First, interdisciplinary teams must create a climate of psychological safety. By doing so, the team will foster increased innovation due to members feeling more comfortable to share novel ideas. Second, team members should listen actively and possess assertive communication styles. Active listening facilitates positive social relationships and clearer understanding of members’ goals and intentions. On the other hand, assertive communication mitigates the potential for members’ diverse expertise to be overlooked. Finally, teams need well-defined shared mental models, so each member knows the strengths of each member as well as a clear vision of the team’s goal.

Melissa Harrell, People Analytics Manager at Google, spoke about the history of Google’s People Operations and the pioneering teams-related research that they conduct. Despite now being well-known for their people-centric culture, founding Googlers initially questioned if team managers even mattered. A series of rigorous studies at Google supported the current science of leadership: effective managers can develop their team members and also ensure their team completes their assigned tasks. The question evolved to what parts of managing could be substituted with data analytics. With this aim in mind, People Analytics eventually created an algorithm that predicted who would be promoted, sometimes achieving up to 90% accuracy and reducing 30% of the time that managers needed to make these promotion decisions. Despite the accuracy of the algorithm and time savings, managers did not respond favorably to this change. As Harrell explained, the algorithm took away the autonomy in these important people-related decisions, and Google eventually stopped using it. This impactful moment in the company’s history led to their current stance: people should not be taken out of people decisions.

With this philosophy, data analytics supplements, but does not replace, the human decisions needed to improve Google’s workforce. Furthermore, this change in perspective has allowed the People Operation’s team to be successful in their teams research endeavors. For instance, Harrell spoke about Project Oxygen, Google’s study into what makes an effective team. Most notably, this project underscored the importance of teams being dependable, having clear structure, and feeling psychologically safe.

Network Research Highlight: Creating Enriched Jobs

By: Elizabeth Moraff

An enriched work design is one in which work roles provide employees with autonomy, task variety, and opportunities to use and develop skills. Despite a wealth of literature pointing towards the benefits of enriched work design, low-quality and poorly-designed jobs continue to pervade the global workspace (Parker, Andrei, & Broeck 2019). Further, relatively little research examines the variables that affect what strategies people use when designing jobs (Parker, Andrei, & Broeck 2019). Work Science Center Network member Sharon K. Parker, with Daniela M. Andrei and Anja den Broeck, sought to ameliorate this gap in a study published in the Journal of Applied Psychology.

Parker and colleagues proposed that supervisors have a tendency towards creating simplified roles while designing work, and that this may lead to the low proportion of enriched work roles. Indeed, they replicated the findings of a 1991 study (Campion & Stevens), in which undergraduate students, when given the opportunity to design clerical roles, overwhelmingly utilized strategies geared towards efficiency and role simplicity, rather than enrichment and enjoyability for workers.

Given this consistent finding, the researchers designed two subsequent studies to explain some of the factors that affect why people choose particular strategies when designing work.

The studies indicated a few important influences on what factors led to more enriched work designs. Firstly, a worker’s current experience of job autonomy corresponded to an increased tendency to design enriching roles for others (Parker, Andrei, & Broeck 2019). Secondly, although registered I-O Psychologists were also more likely to create more enriched roles, this inclination likely stemmed from work experience, which bred implicit knowledge and practical skills, rather than explicit knowledge from their training (Parker, Andrei, & Broeck 2019). In other words, even though I-O Psychologists are experts in work design, they used more enriching strategies not because they were specifically taught to, but because they had experiences that promoted this behavior. Further, openness to change did positively correlate with more enriching strategies and task allocation. Participants who ranked lower on openness to change tended to design less enriched roles (Parker, Andrei, & Broeck 2019).

We are excited for the implications these findings have for IO Psychologists, particularly those involved in designing work and influencing the processes in which work roles emerge. As the Work Science Center, we are glad to highlight research that builds our understanding of how to equip people to design more enriching work.

References

Campion, M. A., & Stevens, M. J. (1991). Neglected questions in job design: How people design jobs, task-job predictability, and influence of training. Journal of Business and Psychology, 6, 169 –191. http://dx.doi .org/10.1007/BF01126707

Parker, S. K., Andrei, D. M., & Van den Broeck, A. (2019). Poor work design begets poor work design: Capacity and willingness antecedents of individual work design behavior. Journal of Applied Psychology. Advance online publication. http://dx.doi.org/10.1037/apl0000383

Workforce Readiness and the Future of Work

Date: Tuesday, January 29, 2019

Network Member, Fred Oswald; WSC Advisory Council member, Tara Behrend; and Lori Foster released the first edition of their edited book, Workforce Readiness and the Future of Work. The book features 13 chapters tackling topics including the roles of technology, education, and policy in modern workforce readiness. The book concludes with a summative and forward-thinking chapter, written by WSC Director, Ruth Kanfer, and Jamai Blivin. 

Healthcare Goes High-Tech

By: Catherine Liu

Modern healthcare organizations are adapting and innovating in response to the boom in artificial intelligence. A recent paper details two distinct branches of use for artificial intelligence in healthcare: virtual and physical.

The virtual branch encompasses the use of deep learning in information management, management of electronic health records, and guidance of physicians in decision making. The virtual branch focuses on technology that can assist healthcare workers by processing and organizing information so less time is spent on menial tasks that could be completed by a computer. For example, electronic medical records make patient information easily accessible to doctors and nurses and allow for important information to be collectively organized in one location. The virtual branch also includes the many applications of machine learning techniques to imaging technology used by radiologists.

In contrast to the virtual branch, the physical branch focuses on tangible technologies that capitalize upon artificial intelligence in order to complete a set of tasks. This can include nanorobots that assist with drug delivery and robots that are used to assist elderly patients. For example, human-interactive robots can provide assistance, guide, and assist with psychological-enrichment with older patients (Shibata et al., 2010).

Although artificial intelligence holds great promise, there is a myriad of societal and ethical complexities that result from the use of artificial intelligence in healthcare, given concerns over reliability, safety, and accountability. As detailed at the Nuffield Council on Bioethics, artificial intelligence currently has many limitations in the medical field. For example, artificial intelligence is reliant on large amounts of data in order to learn how to behave, but the current availability and quality of medical data may not be sufficient for this purpose. Artificial intelligence may also propagate inequalities in healthcare if trained on biased data and may negatively affect patients. For example, a recent study found that men and women receive different treatment after heart attacks. Thus, if the training data did not account for this difference and included primarily male patients, the treatment suggestions given by the artificial intelligence program would be biased and thus may negatively affect female patients. On a practical note, artificial intelligence is limited by computing power, so the large, complex datasets inherent to healthcare may present a challenge, particularly for those organizations that do not have the financial resources to purchase and maintain computers capable of these calculations. Lastly, artificially intelligent systems may lack the empathy or ability to process a complex situation in order to ensure the correct suggestions for what further treatments should be pursued, as in the case of palliative care.

Rather than using artificial intelligence independently or completely abandoning it, combining the predictions made from machine learning algorithms with the expertise and empathy of healthcare providers may allow for better, more comprehensive treatment overall as we head into the future of modern healthcare.

Network Research Highlight: Vocational Interests and Fit

By: Keaton Fletcher

Members of the Work Science Center Advisory Council, Tara Behrend and David Blustein, recently published a groundbreaking study, led by Alexander Glosenberg, in the Journal of Vocational Behavior exploring the fit between individuals’ vocational interests and their current careers across the globe. Vocational interests are essentially common aspects of jobs or careers that may be particularly attractive to individuals. One model, RIASEC (Holland, 1997), breaks these potential interests into Realistic (preference for hands-on tasks), Investigative (preference for scientific inquiry), Artistic (preference for ambiguity), Social (preference for interpersonal interactions), Enterprising (preference for business-oriented activities), and Conventional (preference for data manipulation). A second model, the Octant model (Tracey, 2002), has a similar, though more nuanced break down of vocational interests that is not entirely different from RIASEC. Taken together, these models suggest interests may vary along two dimensions, a preference for data versus ideas and a preference for things versus people. Glosenberg and colleagues used these two models and their combined understanding to explore the nature of vocational interests and person-vocation fit.

Using a final sample of over 63,000 employed individuals from 74 countries/territories, the authors found individuals with higher levels of education are more likely to have a career that fits their vocational interests. This is even more so the case in countries highly individualistic countries and countries with high levels of economic development. Further, the authors found that one of the main models of vocational interests may not hold up as well in less economically developed countries, potentially because work focusing on data and ideas is not as accessible as it is in developed countries.

Work Across the Lifespan

Date: Tuesday, January 22, 2019

Network members Cort Rudolph, Hannes Zacher, and Boris Baltes released their edited book, Work Across the LifespanThe book features 26 chapters, including work from many Work Science Center Network Members. Content ranges from theoretical perspectives on aging at work to applications of a lifespan perspective on job design, performance management, and team dynamics.

The Science Behind Uber’s Nudges

By: Brian Hengesbaugh

Behavioral science has long been used by media and advertisers to influence the decision-making of consumers (e.g., pricing items at 99 cents instead of the full dollar). The growing “gig economy,” in which temporary jobs are completed by independent contractors instead of full-time employees, has led employers to look towards behavioral science concepts in an effort to increase their influence over gig workers.

The New York Times article, ‘How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons,’ explains that the Uber drivers’ status as independent business owners leads to significant cost savings for the company, however, a key trade-off is that Uber cannot control the times or locations that the drivers choose to work. This lack of control over drivers’ schedules can lead to the company’s inability to meet customer demand at peak times (e.g., rush hour) and peak locations (e.g., concert venues). In these situations, Uber currently uses a price increase called surge-pricing to reduce customer demand and entice more drivers to get behind the wheel. The challenge is that surge-pricing scenarios are bad for Uber, but good for drivers. When prices increase, Uber is losing potential customers and current customers face longer wait-times between rides – not good for Uber. For the drivers, the price increase leads to greater compensation per ride and shorter wait time between rides, which increases their effective hourly rate – good for drivers. The misalignment of incentives between the company and the drivers, coupled with the lack of control over drivers’ schedules, has lead Uber to explore methods other than surge pricing to influence the decision-making of drivers. Below is an examination of three interventions, and the associated behavioral science concepts, that Uber has used in an effort to influence their drivers.

Intervention: Almost There!

Goal: Extend shift length

Description: When drivers attempt to log-off, the app sends them a message stating that they are only a specific number of dollars short of reaching an arbitrarily set income goal for the day.

Behavioral Science Concept: Loss Framing – Prospect theory states that people are motivated more by the threat of loss than by the potential of equivalent gain (Kahneman & Tversky, 1979). As a result, people are more likely to take risks to avoid losses than to secure gains. The message from the app frames the daily income as a loss relative to the arbitrary income goal and the motivation of loss aversion leads the driver to continue their shift.


Intervention: Forward Dispatch (or Auto-Queuing)

Goal: Extend shift length

Description: Uber pre-loads the driver’s next ride before the current ride has ended.

Behavioral Science Concept: Regret Avoidance – People feel greater regret for bad outcomes that are produced by new actions than for bad outcomes that result from inaction, and therefore exhibit preferences for inaction (Kahneman & Tversky, 1982). By pre-loading the next ride, Uber has created a scenario in which the driver’s inaction results in continuing the shift.


Intervention: New Driver Signing Bonus

Goal: Reduce attrition rate of new drivers.

Description: New drivers are given a financial bonus when they reach 25 rides.

Behavioral Science Concept: Sunk Cost Fallacy – The investment of time, money, or effort produces a greater tendency to continue an endeavor (Arkes & Blumer, 1985). In order to reach 25 rides, the driver will have invested a significant amount of time and effort in the process and will be more likely to continue working as a driver. As the “gig economy” continues to grow employers will continue to explore new ways to use behavioral science to increase control over independent contractors. Clear ethical guidelines must be developed to help companies navigate scenarios in which there is a misalignment of incentives, or an asymmetry of information, between the company and workers.


Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263-291.

Kahneman, D., & Tversky, A. (1982). The psychology of preference. Scientific American, 246, 160-173.

Arkes, H. R., & Blumer, C. (1985), The psychology of sunk costs. Organizational Behavior and Human Decision Processes, 35, 124-140

Network Research Highlight: Understanding Empathy with Malissa Clark

By: Keaton Fletcher

In a recent review accepted for publication in the Journal of Organizational Behavior, Dr. Malissa Clark (Work Science Center Network Member) and colleagues provide a clearer understanding of the nature and role of empathy in the workplace. Empathy is a complex phenomenon with affective (e.g., experiencing others’ emotions), behavioral (e.g., demonstrating you share another’s internal state), and cognitive (e.g., understanding others’ thoughts) components. People can vary in their trait-levels of empathy, in other words, some people are more empathetic than others, but can also vary moment-to-moment in their empathic state (i.e., I’m more empathetic now than I was this morning).

Clark and colleagues suggest that the proposed link between empathy and improved job performance may be premature; that this relationship may not be as clear as once thought. Specifically, much of the literature confuses empathy (i.e., experiencing the same state as another individual) and sympathy (i.e., understanding, but not experiencing another’s state) sometimes capturing both. So we do not really know if more empathetic people actually are more likely to perform better, or go above and beyond for their colleagues and company. Research does, however, suggest that people view more empathetic individuals as better leaders. There is also promising initial evidence reviewed by Clark and colleagues that suggests empathy may be able to be manipulated. In the remainder of their manuscript, Clark and colleagues outline goals for future researchers on how to improve our understanding of empathy.

Millennial cyberloafing: Why it’s costly & how to approach the problem

By: Jacqueline Jung

With access to technology and the internet nearly ubiquitous in the modern workforce, organizations are struggling with a relatively new phenomenon: cyberloafing. Cyberloafing is the use of technology at work for non-work-related purposes (e.g., checking social media, watching YouTube videos). Cyberloafing may reduce productivity and has been estimated to cost U.S. organizations $85 billion annually (Zakrzewski, 2016). On the other hand, employees born between 1981 and 1995 (i.e., Millenials), grew up with the internet and constant access to technology, and may, to some extent, expect to have this continued liberty at work. The question then remains: how can organizations mitigate the negative effects of cyberloafing while still attracting and retaining millennials, who will soon make up the majority of the U.S. workforce?

For millennials, technology may be viewed as inseparable from communication and entertainment; texting is the standard mode of communication, and sporting events, music, and games can all be accessed through a smartphone (PEW Research, 2009). Millennials also prefer to use the internet to learn new information, more so than their colleagues from previous generations who prefer traditional, structured training (Prosperio & Gioia, 2007). Millennials also do not hold the same work values as other generations–they view work as less important to their identity and place a stronger priority on leisure and work-life balance compared to previous generations (Twenge, Campbell, Hoffman & Lance, 2010). Taken together, this suggests that addressing cyberloafing may be particularly challenging when considering Millennial employees.

Two opposing organizational approaches toward cyberloafing organizations are deterrence and laissez-faire. Deterrence policies limit technology use through stringent monitoring and surveillance, while laissez-faire policies encourage little to no interference or surveillance from the company. 66% of firms claim to monitor Internet use at work (American Management Association, 2008), and while regulation may increase productivity, too much can be counterproductive (e.g., Henle, Kohut, Booth, 2009). Deterrence strategies, such as stringent technology use policies may lead to millennials’ erosion of trust in the organization because surveillance is viewed as an indication of distrust, and millennials view technology as a right that should not be blocked (Coker, 2013). Strict monitoring may also be seen as an encroachment upon Millennials’ desire for work-life balance. Therefore, a zero tolerance for personal technology use may make it difficult to attract Millennials to an organization and may increase turnover intentions among Millenials within the organization (e.g., Henle et al., 2009).

A laissez-faire approach, on the other hand, leaves employees susceptible to the myriad of negative outcomes of technological distractions. Henle and colleagues (2009) suggest that technology may reduce individuals’ attention toward their tasks, and cyberloafing may reduce the amount of time individuals have to complete their tasks, thereby increasing employee stress. Ultimately, employees’ unrestricted access to personal technology use may lead to a decline in organizational performance (Raisch, 2009).

There are viable solutions, however. For example, organizations can establish a clear technology use policy and train millennials as well as their managers on both the benefits and drawbacks of personal technology use at work. When seeking to create this policy, organizations should form an internal committee that includes employees in order to reach an agreed-upon and mutually beneficial stance. This may reduce the likelihood that employees will react negatively to the final policy, since they were a part of its creation (Corgnet, Hernan-Gonzalez & McCarter, 2015). Finally, organizations must provide relevant training on policies and best practices to both employees and managers to ensure standardization and compliance.

References

Coker, B. (2013). Workplace internet leisure browsing. Human Performance, 26(2), 114-125.

Corgnet, B., Hernan-Gonzalez R., & McCarter, M. W. (2015). The role of decision-making regime on cooperation in a workgroup social dilemma: An examination of cyberloafing. Games, 6, 588-603.

“Generations Online in 2009.” Pew Research Center, Washington D.C. (January 28, 2009). http://www.pewinternet.org/2009/01/28/generations-online-in-2009/.

Kim, S. (2018). Managing millennials’ personal use of technology at work. Business Horizons, 61(2), 261-270.

Proserpio, L. & Gioia, D. (2007). Teaching the virtual generation. Academy of Management Learning & Education, 6(1), 69-80.

Raisch, S. (2009). Organizational ambidexterity: Balancing exploitation and exploration for sustained performance. Organization Science, 20(4), 685-695.

Twenge, J., Campbell, S., Hoffman, B., & Lance, C. (2010). Generational differences in work values: Leisure and extrinsic values increasing, social and intrinsic values decreasing. Journal of Management, 36(5), 1117-1142.

Zakrzewski, J. L. (2016). Using iPads to your advantage. Mathematics Teaching in the Middle School, 21(8), 480-483.