Tag: the modern workforce

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

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.

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.

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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.

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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.

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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

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.

By: Catherine Liu

With advances in technology increasing, the need for rapid adaptation and adjustment, many companies, particularly those in the technology sector, have turned toward Agile as a potential solution. In a 2011 study of over 200 IT and business executives, it was found that Agile had a positive, significant correlation with firm performance.

Agile is a mindset developed for software development that emphasizes incremental delivery, team collaboration, and continuous planning and learning. As Agile development becomes relevant to nearly all aspects of the daily workings of companies and not just to areas focused on software development, it is important to understand the core values of Agile methodology. Agile was first developed in 2001 in the Agile Manifesto. The Agile Manifesto established principles that emphasize individuals and interactions over processes and tools, a working product over comprehensive documents, collaborating with customers rather than contract negotiation, and responding to change rather than following a structured plan. Agile was designed to boost the motivation and productivity of teams and to increase the quality and speed that the product is delivered to the market.

Agile focuses on continuous improvement and clear future plans that are malleable based on the situations that arise. Although Agile emphasizes being responsive to change, it does not mean that no planning should be done. Rather, it underscores the importance of continuous planning and revision throughout the project. By continuously planning for the future of the project, the team is able to adapt faster and learn from mistakes that have been made. Agile focuses on the Definition of Done, which is a list of criteria based on project goals which must be met before a section of a product is considered to be completed.

Specifically, Agile teams form when a project is presented. Teams consist of a lead who works on overall project management, team members who work on the technical aspects of the project, and a product owner who helps make a prioritized work item list. Agile projects cycle through a process of (1) reviewing requirements, (2) planning the next steps, (3) designing a the solution, (4) developing the solution, (5) releasing the product for testing , and (6) tracking and monitoring the product’s usage in order to find bugs to fix, before restarting the cycle and reviewing the new requirements of the project based on the bugs found. Lastly, Agile teams typically dissipate when the project is completed, and team members can join other teams.

Agile has been used successfully in companies, such as Apple, Microsoft, IBM, and AT&T and is being adopted into companies that are less technology focused, such as McKinsey & Company. Agile methodologies can be applied to nearly all disciplines, not just to software development. In a 2016 Harvard Business Review article, the application of Agile to multiple sectors such as marketing, human resources, and warehousing is discussed. Agile, when adapted properly, gives companies the ability to revolutionize their productivity, worker satisfaction, and product quality.

By Brian Hengesbaugh

Why aren’t there more women on corporate boards? Women constitute 47% of the labor force and 52% of management and professional positions (Bureau of Labor Statistics, 2017). Yet women comprise just 21% of corporate board seats (Catalyst, 2018). 

This dearth of women on corporate boards exists despite what appear to be strong efforts to the contrary. In 2009, the Security and Exchange Commission (S.E.C.) ruled that publicly traded companies need to disclose how diversity factors into the selection process for directors. Moreover, a pair of surveys in 2012 showed that 75% of U.S.-based publicly traded companies had instituted diversity policies, and 80% believed that diversity in the boardroom created shareholder value. It seems that diversity policies and the belief in the importance of diversity is not enough. 

A 2017 article, published by Catherine Tinsley and colleagues, explores the decision making factors that influence corporate board selection. Classic decision making indicates the use of a multi-attribute decision making model to: identify the selection criteria, weight each criterion based on relative importance, and select candidates based on performance against these weighted factors – a process known by I-O Psychologists as mechanical combination. However, these decision aids are infrequently used in practice. HR managers and head-hunters often believe that using their “gut instinct” produces better results. This preference for instinct over analytic tools increases with experience (Camerer and Johnson, 1991).

In the absence of external decision making aids, we regularly rely on rules, known as heuristics, to simplify complex decisions. These mental shortcuts often operate nonconsciously to ease the cognitive burden of a decision. Tinsley posits that the percentage of women on corporate boards may be slow to increase, despite the presence of positive attitudes towards gender diversity, due to the use of a gender-matching heuristic. 

Gender-matching refers to the propensity to match the gender of the incoming candidate to the gender of the board member being replaced. The results of the analysis of archival board data from 2002-2011 for more than 3,000 U.S.-based publicly traded firms showed that a woman is most likely to be selected to join a board when a woman has just left the position. On average, 12.8% of new board members are women. This number drops to 10% when replacing a man, and increases to 23% when replacing a woman – nearly doubling the rate at which women are selected (Tinsley, Wade, Main, & O’Reilly, 2016). 

While the propensity for gender-matching remained robust, Tinsley’s subsequent laboratory studies found that fewer than 10% of participants cited gender-matching as a criteria for board member selection. This indicates that the gender-matching heuristic is primarily operating outside of conscious awareness. 

Further laboratory studies by Tinsley and colleagues sought to understand “what works” to increase the representation of women on boards. These studies examined two factors: (1) highlighting the importance of gender diversity and (2) manipulating the gender composition of the candidate pool. Results showed that priming decision makers by highlighting the urgency of selecting a woman had little impact on improving gender diversity. However, there was a significant increase in the selection rate of women when the candidate pool was comprised of more women than men, suggesting a need to more actively recruit female applicants if gender-diversity is valued by an organization. 

Additional research is needed to explore the nonconscious mechanisms of the gender-replacement heuristic, as well as understand the factors that work to increase the selection of women to corporate boards. Practitioners should explore methods of increasing the female to male ratio of applicants by examining qualities of job ads, recruiters, or company culture that attract women applicants.

Key takeaways:
Women are underrepresented on corporate boards.
Women are most likely to be selected as a new board member when the board member being replaced is a woman.
Increasing the number of women in the candidate pool increases the rate at which women are selected.

References:

Camerer, C. F., & Johnson, E. J. (1991). The process-performance paradox in expert
judgment: How can experts know so much and predict so badly? In W. M. Goldstein & R. M. Hogarth (Eds.), Research on Judgment and Decision Making: Currents, Connections, and Controversies, pp. 342-364. Cambridge, UK: Cambridge University Press. 

Catalyst. (2018, October). Pyramid: Women in S&P 500 Companies. Accessed at 
https://www.catalyst.org/knowledge/women-sp-500-companies (October 2018)

Tinsley, C. H., Wade, J., Main, B. G. M., & O’Reilly, C. A. (2016). Gender Diversity on U.S. 
Corporate Boards: Are We Running in Place? ILR Review, 70 (1), 160-189

US Department of Labor, Bureau of Labor Statistics. (2017, April). Women in the labor force: 
a databook. Accessed at 
https://www.bls.gov/opub/reports/womens-databook/2016/home.htm (October 2018).
 

By: Hannah Ramil

The Ethic of Care (EoC) rests upon the belief that “an awareness of the connection between people gives rise to a recognition of responsibility for one another, a perception of the need for response” (Gilligan, 1982). In essence, the EoC perspective emphasizes the importance of interpersonal relationships and the needs of others in moral reasoning and moral decision-making.

Previous studies have found that care and compassion in the workplace can enhance commitment to the organization (Lilius, Kanov, Dutton, Worline, & Maitlis, 2012), workplace self-esteem (McAllister & Bigley, 2002), and resilience (Waldman, Carmeli, & Halevi, 2011), and reduce work-based anxiety (Kahn, 2001). Building on these previous findings, Lawrence and Maitlis (2012) proposed the EoC as an underpinning for narrative practices in the workplace. They suggested that narrative story-telling of shared experiences, struggles, and possible futures amongst coworkers can be a vehicle for enacting care in the workplace. For example, when a work team debriefs about a recent performance episode, members can take this time to appreciate and acknowledge one another’s abilities and commitments. This practice can lead to group potency, a shared belief among team members in the general efficacy of the team as a whole (Guzzo, Yost, Campbell, & Shea, 1993; Lester, Meglino, & Korsgaard, 2002).

Carmeli and colleagues (2017) empirically examined the EoC perspective as a corporate culture. Corporate culture is “a set of norms and values that are widely shared and strongly held throughout the organization” (O’Reilly and Chatman, 1996, p. 166). These shared norms and values influence worker attitudes (e.g., job satisfaction and organizational identification) and behaviors (e.g,. task performance and counterproductive work behaviors). Their study focused on how corporate culture may influence workers’ likelihood of engaging in sustainability-related behaviors, such as prioritizing environmental concerns, choosing more sustainable alternatives for products, services, and practices, lobbying, activism, and encouraging sustainable behaviors throughout the company (Carmeli et al., 2017).

In their first study, they found that an organizational culture based on EoC increased employees’ satisfaction with the organization’s sustainability concerns and increased employee motivation to follow through with the organization’s sustainability values. This boosted employee involvement in sustainability-related behaviors. Their second study found that EoC was related to increased employee sustainability-related behaviors. Not only did EoC improve sustainability behaviors, but it also enhanced the employees’ identification with the organization.

For organizations hoping to increase sustainability efforts (e.g., WeWork’s new meatless initiative), establishing a corporate culture founded on an EoC may help employee adherence to initiatives.

By: Brian Hengesbaugh

How can we enhance goal setting and increase performance? Prime the mind with effort.

Priming is the process of using a stimulus to subconsciously activate stored knowledge and psychological processes. As an example, if you were to read the following sentence “The fire truck ran through the intersection, ignoring the stop sign” and then were asked to think of a color, any color, you would most likely think of red. At a neuronal and cognitive level, our minds hold networks of interconnected ideas, and activating one node in the network, primes the other nodes for activation. We can capitalize upon this to improve performance.

In an experiment conducted by Latham and colleagues, pictures of weightlifters lifting various weights were used as the stimulus to prime participants with different levels of effort for the upcoming task of pressing on a scale. Before performing the task, participants either saw a picture of a weightlifter lifting 20 lbs (easy goal), 200 lbs (moderate goal), or 400 lbs (difficult goal). The study found that participants who were primed with the picture of the weightlifter lifting 400 lbs (difficult goal) exerted more effort during the task than the participants who were primed with the moderate and easy goals. A following experiment by Latham, which used a brainstorming task, showed that participants primed with the difficult goal consciously chose to set more challenging goals for themselves and performed better than the participants primed with easier goals. 

What about using goal priming in the workplace? Shantz and Latham primed workers at a call center with a picture of a woman winning a race. Employee performance was then measured based on the amount of pledged donation dollars raised during the subsequent three-hour shift. When compared to the control group that did not receive a prime, the group of primed workers raised significantly more money than the control group. Latham and Piccolo, later showed that priming the call center workers with an image of three smiling individuals on headsets, improved performance above and beyond the general success prime.

More research is needed in order to thoroughly understand the subconscious processes of goal priming. Bargh’s automaticity model, which explains the relationship between primed goals and performance, has been criticized for a lack of theoretical framework describing the mechanisms that link the subconscious priming stimulus to behavioral changes. Latham and his colleagues look to fill this void with Goal Setting Theory – an explanation of the positive relationship between the difficulty of a consciously set goal and performance towards that goal, with factors such as effort, persistence, choice, strategy, and conscientiousness used to mediate the relationship. 

As research continues, two key takeaways remain: 
(1)    Effort levels can be primed
(2)    Increased effort leads to increased performance