Tracking the Effects of Technology-Automation Disruptions on Human Work

Author: Xinyu Chu, Catherine Liu, Pooja Juvekar

Many industry leaders believe we are currently at the peak of the fourth industrial revolution (The World Economic Forum, 2016). Unlike previous industrial revolutions, in which technology was used to streamline production, aiding with simple, specific, routinized tasks, modern technologies have enabled automation of a much wider range of functions. To better understand the effects of these disruptors, we first need to define and clarify key terms that are often (mis)used.  

Automation refers to the performance of a process in absence of human participation (The World Economic Forum, 2016).
Automated teller machines (ATMs) complete transactions without the participation of bank staff.
Artificial Intelligence 
Artificial Intelligence (AI) is a computational method of converting environmental inputs into actions or outputs in a way that mirrors human cognitive functioning (Shahin, 2016, p.33)
Siri can recognize and interpret human speech and provide relevant answers or actions. 

A robot is a computer programmed machine that is able to sense and gather information of the surrounding environment to build a simulation model so that it can perform its action automatically (Kuipers, 2018).
Roomba, a robot vacuum cleaner, is able to autonomously navigate and clean one’s floors.
The Internet of Things 
Physical objects can be equipped with electronics, sensors, and network connectivity that allow them to communicate with one another. This interconnection of objects is the internet of things. (Whitmore et al., 2015)
Thermostats, home lighting, and background music can all be controlled and monitored by apps on a smartphone.

Cloud Computing
Cloud computing connects multiple remote servers or computers in a network to carry out the functions normally performed by an isolated computer.
(Mell & Grance, 2011)
Programs like Google Docs or Dropbox provide online cloud space for people to store their documents, so that they can work on them at anytime and in any device.  

Machine Learning 
Machine learning is a type of AI that allows for data-driven improvements in prediction and performance, without the need for additional human programing (Samuel, 1958)
Online retailers like Amazon use machine learning to make product recommendation to customers based on their previous purchases or searches on items. 

Deep Learning 
Deep learning is a set of machine learning techniques, that relies on multiple levels of processing that progressively convert raw data into increasingly usable and abstract outputs (Bengio, Courville, & Vincent, 2013) 
Xu and colleagues (2015) created a program that could analyze photographic images and develop text captions that can precisely and briefly describe them. 

Artificial Neural Networks
An artificial neural network is a collection of nodes (computational units that convert inputs to outputs with a programmed function) that represent different attributes of the system being modeled.
Anderer and colleagues (1995) used an artificial neural network to identify dementia from an analysis of EEG patterns.

Big Data
Big Data is a large volume of data collected from an internal and external organization that may be used for analysis to trends and associations in patterns. Big data is marked by a large volume, high velocity of new data, and a wide variety of data input.
Different industries such as retail, healthcare, and education use big data to detect trends in their customers and compare accordingly, in purchasing habits or prolonged educational performance. 

Data Science
Data science is the process of capturing, maintaining, processing, analyzing, and communicating data (Berkeley Data Science).
Internet search engines use data science to deliver search queries and find the best results for searches.
Natural Language Processing
Natural language processing is a form of artificial intelligence that allows computers to understand, interpret, and manipulate human language (SAS, analytics company).
Electronics, such as the google home and amazon echo, are able to understand and respond to our commands.
Computer Vision
Computer vision is the theory and technology for building artificial system that obtain information from images or multi-dimensional data (Science Daily).
Snapchat filters calculate the distance between objects and the relative positions of elements in a stream of images from the user’s camera.

Data Mining
Data mining is the process of finding patterns and relationships within large data sets to predict outcomes (SAS, analytics company).
E-commerce sites use data mining to advertise similar products to consumers and predict what other products consumer may consider purchasing.
Virtual Reality
Virtual reality is a computer-generated artificial three-dimensional environment where an user can interact with when equipped with electronics, such as a helmet with a screen inside or gloves with sensors (Merriam-Webster).
Lowe’s, a home improvement store, created a virtual reality based skills-training clinic to teach participants visually and more realistically in a 3D environment.

New technologies provide the power for different forms of automation.  Deep learning, for example, is a method of machine learning, which is in turn a critical component of the broader field of artificial intelligence, which tends to rely heavily on cloud computing and data science to rapidly complete necessary calculations. Many modern robots as well as the internet of things, rely on machine learning, cloud computing, and aspects of artificial intelligence to enhance the user experience and further increase data processing speeds. Taken together these technologies work in tandem to automate different aspects of production and human work. 

Automation effects on the human working experience
Although organizations have long relied on automation in production, new automated tools are being implemented ever more successfully across a range of industry sectors.  For example, BMW has introduced more than 2000 robots and smart technology to their manufacturing plant in Spartanburg, South Carolina (Mitchell, 2018). Previously, robots were cordoned off in areas inaccessible to humans because of safety concerns.  But greater attention to the human-robot interface has made it possible for a more seamless integration of robots and humans, an integration in which robots often take over the repetitive and routinized assembly line tasks that pose strain and safety issues for human workers (Allinson, 2017). One robot, referred to as Miss Charlotte by the employees, has been integrated as part of the team, entirely taking over a difficult task. Workers typically feel more personal responsibility towards a task if they are interacting with machine-like robot subordinate (Hinds et al., 2014).

Advances in artificial intelligence are increasing the use of machine learning in healthcare. Machine learning tools have shown promise in diagnosing skin cancer and many rare diseases (Sennaar, 2018). A new machine learning algorithm processed millions of previous digital medical records to generate an accurate diagnosis for patients within a few seconds (Jiang, 2017). If these algorithms become widespread, they may shift the role of physicians from diagnosticians to physical and psychosocial healers. Further, the collaboration between AI and doctors may not only improve the quality of the diagnoses, but also shorten the waiting time for patients and increase access to quality healthcare. To capitalize upon this technology, machine learning algorithms must be seen as trustworthy by the physicians who may otherwise ignore them in favor of their own clinical expertise. This requires transparency in the decision making process, including what information the decision was based upon and what the probabilities associated with different diagnoses may be. One study found that when presented with more transparency about the plans, rationale, and possible outcomes of actions conducted by AI agents, human operators were generally more trusting and willing to incorporate the results of the AI agent into their problem-solving process (Chen, 2016). The study also showed a positive relationship between transparency of the intentions behind the actions and people’s task performance in terms of decision making and job operation. Furthermore, Chen (2016) argues that transparency about behavioral intention can contribute to the improvement of the machine learning process of the AI agents, as it provides human operators to evaluate and analyze the information presented by the AI agents, thus including human operators in the decision making process.

The implementation of new technologies in the marketplace is also changing the work experience of office personnel.  For example, Alexa for Business can integrate workers’ calendars, personal phones, meeting room equipment, and other smart technology across physical locations to create an IoT that simplifies the work experience (Walker, 2017).  Advances in artificial intelligence as well as the IoT allow for a smart office that eliminates some daily hassles which can contribute to making work stressful or unpleasant. By integrating personal information from multiple devices, tasks that previously were complicated and bothersome (e.g., finding a time and location for everyone to meet) can be offloaded to technology and done almost instantly. 

Implications of automation on human jobs

As automation becomes more common, there has been increased attention paid to the human cost in terms of jobs eliminated or altered. Acemoglu and Restrepo (2017) estimated that for each robot introduced to the workplace, six people would lose their jobs. A report by McKinsey Global Institute estimated that by 2030, 50% of our time currently spent on work would be replaced by automation, and up to 30% of current work activities would be automated. 

However, as some jobs disappear, others are created or expanded by automation and new technologies. Rideshare drivers (e.g., Uber and Lyft), for example, would not exist if it were not for the automation and programming available today. Similarly, the introduction of robots into BMW’s facilities necessitated the creation of an apprentice mechanic program to fill the many open equipment service associate positions (Mitchell, 2018). 

If past industrial revolutions serve as a guide, we would expect automation to create many new jobs that currently don’t exist.  However, how many jobs will be created and the skill requirements associated with performing such jobs are still largely unknown.  Therefore, we focus on more concrete questions, such as : (1) What jobs are least likely to be substituted by automation? (2) What is the likely pattern of task reallocation among existing jobs as automation becomes more prevalent across industry sectors? 

Frey and Osborne (2017) suggest three task domains in which humans have comparative advantage over robots: (1) perception and manipulation tasks, (2) creative intelligence tasks, and (3) social intelligence tasks.  Humans’ ability to rapidly sense, perceive, and understand the world around them is currently unmatched in many ways by technology. This ability enables a range of complex behaviors from handling irregular objects to interacting with dynamic and unpredictable environments.  Jobs that rely on these skills (e.g., daycare provider, nurse) should be relatively immune (at least in the near-term) to the effects of automation.  For example, although there is a push for the integration of artificial intelligence in healthcare, the trust between clinicians and patients is nearly impossible to replicate (Wahl et. al, 2018).  Further, jobs that require the creation of novel ideas, ranging from poems to scientific discoveries, while enhanced by automation, should also be safe. Automation may actually make these jobs more attainable for individuals who would not normally have access to the training or technology traditionally necessary to succeed. Lastly, jobs that require sophisticated social interactions like negotiation and persuasion should also be relatively secure, given that they focus on tasks such as complicated human interactions, development of novel ideas, quick capture of visual and auditory signals, and complicated body and facial expression. This includes many jobs in management, finance, media, and art.   

With the increasing demand for humans with strong “soft” or people skills, we expect a rise in the number of mixed human/robot teams.  For example, financial managers may spend less time on data processing, which can be handled easily by artificial intelligence, instead shifting their focus toward managing employees and interacting with stakeholders and business partners. 

Promising Research Directions

Although automation is often regarded by workers as a threat to job displacement, it is also the case that automation may improve the human work experience by reducing the number of dangerous, monotonous, or minimally rewarding tasks that employees must perform. We need to further examine strategies or interventions to facilitate and improve this type of interaction so that it provides an advantage to both employees and organizations.  

Automation engineering is only one aspect of the industrial revolution.  To be successful from the human perspective, more research is needed to understand how automation and the process by which it is implemented affects the human work experience.  For example, how best can employees and automated programs or robots coordinate and communicate to avoid errors and redundancies? Also, how can we best select, train, and support employees who will be required to coordinate and communicate with automated technology? Lastly, as much of the focus on technology in the workplace is centered on selection, hiring, and performance appraisal, it is vital that the team dynamic between technology and employees is analyzed in the near future, especially as automated technology becomes a large part of many people’s day to day interactions in their respective workplaces.

Three takeaways on technology-automation disruption: 

The implementation of new technologies in the form of automated machines, agents, and robots will have multiple effects on the human labor market as some jobs are automated out of the market, new jobs are created, and yet other jobs are changed.
The current limits of technology and automation suggest that human jobs will change in   ways that can foster a more meaningful work experience (e.g., jobs that involve more social interaction, creativity, and challenge). 

Successful integration of automated technologies requires that employers consider not only the design, purpose, and functionality of new tools but also how they interface with human workers and their effect on team functioning. 
 Further Reading: 

Acemoglu, D., & Autor, D. (2011). Skills, Tasks and Technologies: Implications for
Employment and Earnings, Handbook of Labor Economics,4,1043-1171

Allinson, M. (2017). BMW shows off its smart factory technologies at its plants worldwide.
Robotics & Automation. 

Bengio, Y., Courville, A., & Vincent, P. (2013). Representation Learning: A Review and New 
Perspectives. IEEE Transactions On Pattern Analysis & Machine Intelligence, 35, 1798-1828. doi:10.1109/TPAMI.2013.50

Bughin, J., Manyika, J., & Woetzel, J. (2017). Job lost, jobs gained: Workforce transitions in a 
time of automation. Retrieved from Mckinsey Website: 

Chen, J. C. (2018). Human-autonomy teaming in military settings. Theoretical Issues
In Ergonomics Science, 19, 255-258. doi:10.1080/1463922X.2017.1397229

Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human
Factors, 37, 32-64. doi:10.1518/001872095779049543

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to
computerisation? Technological Forecasting and Social Change,114,254-280

Hinds, P. J., Roberts, T. L., & Jones, H. (2004). Whose Job Is It Anyway? A Study of
Human-Robot Interaction in a Collaborative Task. Human-Computer Interaction,19(1/2), 

How Human-Robot Teamwork Will Upend Manufacturing (2014, Sept 16). Retrieved from

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y.
(2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology 2. doi:10.1136/svn-2017-000101

Kuipers, B. (2018). How Can We Trust a Robot?. Communications of The ACM, 61, 86-95.

Lee, S. H., Chan, C. S., Mayo, S. J., & Remagnino, P. (2017). How deep learning extracts and     learns leaf features for plant classification. Pattern Recognition, 71, 1-13. doi:10.1016/        j.patcog.2017.05.015

Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. [electronic resource]. 
Gaithersburg, MD : Computer Security Division, Information Technology Laboratory, National Institute of Standards and Technology.

Mitchell., A. B. (2018). BMW seeks more humans to maintain Greer plant’s robots. Greenville 

Samuel, A. (1969). Some studies in machine learning using the game of checkers. II—Recent 
progress. Annual Review in Automatic Programming, 6 (Part 1), 1-36. 

Sennaar, K. (2018). Machine learning for medical diagnostics — 4 current applications. Techemergence. 

Shahin, M. A. (2016). State-of-the-art review of some artificial intelligence applications in pile
    foundations doi:

The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning.
(2017, December 4). Retrieved from

Thomas, C., Stankiewicz, L., Grötsch, A., Wischniewski, S., Deuse, J., & Kuhlenkötter, B.
(2016).  Intuitive Work Assistance by Reciprocal Human-robot Interaction in the Subject 
Area of Direct Human-robot Collaboration. Procedia CIRP, 44(6), 275-280. doi:10.1016/j.procir.2016.02.098

Walker, T. (2017). Announcing Alexa for Business: Using Amazon Alexa’s voice enabled
devices for workplaces. AWS News Blog. 

World Economic Forum(2016), The Future of Jobs: Employment Skills and Workforce
Strategy for the Fourth Industrial Revolution. Retrieved from World Economic Forum website: 

What is Cloud Computing: a beginner’s guide. (2018, January 28). Retrieved from

Who Needs the Internet of Things? (2016, September 13). Retrieved from 

Whitmore, A., Agarwal, A., & Xu, L. (2015). The Internet of Things-A survey of topics and     trends. Information Systems Frontiers, 17(2), 261-274. doi:10.1007/s10796-014-9489-2

Xu, K.; Ba, J.; Kiros, R.; Cho, K.; Courville, A.; Salakhutdinov, R.; Zemel, R.; and Bengio, Y.
    (2015). Show, attend and tell: Neural image caption generation with visual attention. In

How to Thrive At Work: Hannes Zacher

Author: Yendi McNeil

What does it mean to thrive at work and how do we achieve this goal? Thriving at work has been described as a positive psychological state accompanied by a sense of vitality and learning (Kleine, Rudolph, & Zacher, 2019), but understanding what leads to, and results from, thriving at work is a much more complex question. Work Science Center Network Member, and Professor of Work and Organizational Psychology at Leipzig University, Hannes Zacher, has tackled this question from a range of different angles including aging within the workforce, the use of selection, optimization, and compensation (SOC) strategies, and the relationship between wisdom and thriving at work.

Wisdom may be one tool that individuals can draw upon to help them thrive at work. Although there is no universally accepted definition, wisdom has been defined as a complex concept in which an unbiased and open attitude combined with self-reflection allows for reliable judgement and profound insight (Zacher & Kunzmann, 2019). Wisdom empowers employees to break from the status quo and well-known traditions and take on more personal risks. Results from a meta-analysis suggest wisdom to be a potential resource that helps address common problems faced in the modern workplace (i.e. job stress, incivility, abusive supervision, and lack of ethical, social and environmental corporate responsibility) (Zacher & Kunzmann, 2019). Gaining wisdom-related knowledge can lead to job crafting and better coping strategies (emotional and motivational) (Zacher & Kunzmann, 2019). Some strategies used to nurture wisdom in the workplace include openly conversing with respected others about life problems, engaging in mental travels while talking about wisdom tasks (intervention), and self-distancing from emotional situations and emphasizing self-focus (Zacher & Kunzmann, 2019). To implement these strategies, companies can provide interventions and mentorship to employees. For example, employees can be trained to be aware of the lifespan context of work problems, or to develop better understanding and perception of problems from different perspectives including those of co-workers. Lastly, individuals can work around uncertainty and gain an understanding of their own abilities. 

Just as wisdom can aid with the expansion of one’s perspective and problem solving, individuals’ self-regulation strategies can help maximize innovative performance, one method of thriving at work. Using SOC strategies actively manages one’s limited resources in taxing situations which can affect outcomes like job satisfaction, job engagement, and job performance (Breevaart & Zacher, 2019). Breevart and Zacher asked ninety-one German employees to complete a set of surveys every day for five workdays. Findings showed that employees make more use of SOC strategies when they are given more job autonomy, leading to more innovative performance. While using SOC strategies, employees develop a sense of agency which enables them to create and implement new innovative ideas. With this sense of agency, employees gain confidence in their ideas and are better able to persuade others to implement them. Time pressure can further enhance the effects of SOC strategies on innovative performance, but only if the time pressure is perceived as a challenge (not a threat). When implementing these findings, employees should be trained to ask for more autonomy in their job (job crafting), and organizations should provide autonomy for their employees to allow for increased use of SOC strategies.

Thriving at work is not just an outcome of our abilities (e.g., wisdom) or skills (e.g., SOC strategies); our age may influence how we thrive and are committed to our work and careers. To explore this, Zacher (2019) conducted a meta-analysis about the relationship between aging and career commitment. Overall, one’s motivation to continue their career increases with age, but conflictingly, more focus is also put on values outside of the individual’s career (e.g., family roles; leisure activities), thereby causing disengagement (Katz, Rudolph, Zacher, 2019). This change may cause a decrease in commitment to one’s career. The drop in commitment as one ages should be interpreted by the individual and employer as a signal for role transition rather than retirement. Moreover, continuance commitment (i.e., a perceived need to stay in an organization) has one of the strongest relationships with age. This may be because employees accumulate and invest resources in the organization over the course of their career. The loss in investments can affect one’s commitment to a career especially at an advanced age. Organizations can increase one’s career commitment by increasing employees’ job autonomy especially for older employees.

In a comprehensive meta-analysis, Anne-Kathrin Kleine and WSC Network Members Cort Rudolph and Hannes Zacher (2019) holistically examined what influences and results from thriving at work. Individual characteristics (Psychological capital, Core self-evaluations, Proactive personality, Positive affect, Work engagement, Negative affect, & Perceived stress), relational characteristics (Heedful relating, Supportive coworker behavior, Workplace civility, Supportive leadership behavior, Empowering leadership behavior, Transformational leadership, Leader-member exchange quality, Perceived organizational support, Trust, & Workplace incivility), and outcomes (Subjective health, Job satisfaction, Commitment, Positive attitudes toward self-development, Task performance, Organizational citizenship behavior, Creative performance, Burnout, & Turnover intentions) were analyzed as predictors of thriving. Overall, thriving at work had strong relationships with psychological capital, proactive personality, work engagement, heedful relating, LMX, and perceived organizational support. In terms of outcomes, thriving at work was a strong predictor of creative performance, job satisfaction, organizational commitment, and predicted lower levels of burnout. Organizations should improve the work conditions in order to foster positive relationships between coworkers, supervisors, and ultimately the company. 

Taken together, the recent work of WSC Network Member, Hannes Zacher, provides a broad understanding of thriving at work. Specifically, to promote thriving at work, organizations should cultivate wisdom-related knowledge and behaviors, increase job autonomy for employees, and improve perceived organizational support and leader-member exchange quality. 

To Maximize Employee Outcomes, Focus on Exchange

Author: E. L. Moraff

Most employers can imagine their dream employee. This employee has valued skills, works hard, believes in the company’s mission, consistently goes above and beyond and so on. Organizations spend precious resources in an effort to successfully identify, hire, and retain these individuals. But understanding how to create a culture that supports, engages, and binds these employees to the organization is often daunting. One logical place to begin relates to creating an environment that individuals perceive to be fair and just. Research findings over the past three decades show that organizational justice perceptions affect an employee’s commitment, satisfaction, performance, and willingness to go the extra mile (Cohen-Charash & Spector, 2001; Colquitt, Conlon, Wesson, Porter, & Ng, 2001). Employees who perceive their organization as unjust are more likely to quit, skip work (Gellatly, 1995), and engage in counterproductive behaviors (e.g., theft, gossiping, bullying; Moorman 1991). 

Leadership makes a different here. It may seem obvious to point out that leaders exert tremendous influence over an employees’ work experience. In many instances, managers can improve employee performance by simply being fair and attentive. As Burton, Sablynski, and Sekiguchi point out, it’s not enough to structurally create a just environment for an employee to thrive. Employees need to have high-quality leader relationships with their leader as well (2008). Research findings on Leader-Member Exchange, show that engaged and mutual interactions between an employee and a supervisor can boost organizational citizenship behaviors and employee performance (Erdogan & Liden, 2006). Put another way, teaching supervisors how to nurture relationships with their subordinates sets the stage for better employee outcomes.

Organizational Justice & Leaders: the ideal environment

Research findings show that employees perceive and distinguish between at least three types of organizational fairness (Cropanzano and Greenberg, 1997). Arguably the most well known type of fairness is distributive justice, which refers to the perceived fairness of how outcomes, such as salary, are distributed among people. Procedural justice refers to the perceived fairness of the process by which outcome distributions, for example, how fair is the performance appraisal process upon which salary increases are based. The final, and somewhat more scientifically controversial form of justice is called interactional justice. This type of justice refers to the extent to which the employee perceives that he/she was treated fairly and respectfully in interpersonal interactions (Burton et. al, 2008). For instance, does the employee perceive that they are being shown dignity and respect during the appraisal discussion? Even if the outcome or process is perceived to be unfair, did the messenger (usually a supervisor) at least provide an adequate explanation? 

As the concept of interactional justice suggests, the communication process itself can have a powerful effect on perceptions of fairness! From a practical perspective, employees are concerned with justice because how they are treated interpersonally is viewed as an index of their social standing in the team and what their outcomes at work might be (Williams, Scandura, Pissaris, & Woods, 2016). Similarly, higher quality employee-supervisor relationships strengthen overall justice perceptions. A leader who has poor communication skills and social sensitivity can impede the organization’s otherwise strong structural justice. Perhaps the organization has great processes, but the leader consistently treats an individual unfairly. The leader will diminish the possibility that employee will achieve stellar outcomes (Burton et. al, 2008).

Leader-Member-Exchange (LMX) theory and research provides important information about how leaders affect employee attitudes and behavior. According to the theory, LMX refers to the “quality of the relationship between a supervisor and an employee; it involves an examination of the dyadic relationships, interactions, and perceptions about the working relationship” (p. XX, Graen & Scandura, 1987). As LMX research suggests, rapport and trust develop through a series of interpersonal exchanges that employees and employers have during the normal course of business. This rapport in turn affects an employee’s perception of organizational justice and a range of work outcomes, including organizational citizenship. (Burton et. al, 2008). 

LMX theory highlights the importance of one-on-one interactions, particularly between supervisor and subordinate. Obviously, that relationship can vary substantially across the organization and even with the same leader. Generally, weak supervisors will have weak LMX with their subordinates, but even strong supervisors may not be able to have high LMX with all supervisees (Erdogan et. al, 2006). Depending on company size, a leader may not have the time or space to engage in frequent enough interactions to develop a high quality relationship (Erdogan et. al, 2006). Even with this limitation, managers can consciously choose to allocate their relational energy with LMX in mind. For instance, maintaining a high level of fairness in interactions with employees can be particularly important for individualistic workers. For workers higher in collectivism, managers need to focus on developing relationships because fairness alone will not boost overall perceptions of organizational justice in collectivist cultures (Erdogan et. al, 2006). 

How to Recognize Successful & Unsuccessful LMX Dyads

Managers looking to identify how employees perceive the state of their relationship can glean clues from employee behavior. How often an employee approaches a supervisor serves as one important indicator. Similarly, when an employee does approach a supervisor or leader, what tactics do they use to influence the leader (Williams et al., 2016)? Research indicates that employees with high quality relationships with their supervisors will use more direct involvement and interaction strategies than employees that perceive a poor relationship with their supervisor. Employees with strong relationships with their supervisor are also more likely to seek out the supervisor and vice versa. Further, employees with a high LMX are more likely to present their point of view using logical arguments and evidence that supports the feasibility and relevance of a request, (Williams et. al, 2016). 

In contrast, employees with a lower quality relationship with their employer are less likely to approach the supervisor directly or alone (Williams et. al, 2016). Instead, such subordinates may try to make their request or viewpoint known by enlisting the help of supportive co-workers who have better relationships with the supervisor (Williams et. al, 2016). Rather than attempt to convince a supervisor of an idea on its own merits, individuals with less high quality relationships to their supervisor are more likely to build a coalition of people to support their idea before presenting it to a person in authority (Yukl and Tracey, 1992). Ingratiating behaviors, intended to curry relational favor with the boss, demonstrate that the employee feels insecure in the relationship, and that they may perceive the supervisor to be generally unjust (Erdogan et. al, 2006). This soft influence tactic may indicate that a manager is coming off as unfair and authoritarian (Erdogan et. al, 2006). 

Last but not least, managers can observe employee behaviors for clues about how the employee regards their relationship to the manager and their perceptions of fairness. High absenteeism, high levels of retaliation, and perceived low commitment to the work serve as definite red flags (Burton et. al, 2008). 

Conclusions & Further Directions

The research findings are clear: employee perceptions of injustice (distributive, procedural, and interactional) in the workplace are associated with poorer work attitudes, performance, and citizenship behaviors. LMX theory and research shows that an important source of justice perceptions, particularly procedural and interactional, arises from the quality of the interpersonal relationship established between the supervisor and employee. High quality relationships, characterized by trust and open dialogue, are likely to promote justice perceptions. 

From a practical perspective, research findings suggest that supervisors can indirectly assess the quality of their relationship with subordinates by considering how often they seek interaction and the strategies by which subordinates present viewpoints and suggestions. 

Yet there are still many gaps in our understanding of how justice perceptions arise from supervisor-subordinate relationships. Additional research is needed to better understand how individual differences in employee traits and attitudes affect perceptions of their relationship to their supervisor and perceptions of fairness, and the pathways by which those perceptions affect job performance and organizational citizenship behaviors. In the interim, the the findings to date suggest that leaders are well-advised to build strong interpersonal relationships with as many subordinates as possible. While this relational investment requires a lot of energy, committed supervisors can expect a more motivated staff team and better individual outcomes. 

Burton, J. P., Sablynski, C. J., & Sekiguchi, T. (2008). Linking Justice, Performance, and Citizenship via Leader-Member Exchange. Journal of Business Psychology 23, pp. 51-61.

Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillside, NJ: Erlbaum.

Cropanzano, R., & Greenberg, J. (1997). Progress in organizational
justice: Tunneling through the maze. In C. L. Cooper & I. T. Robertson (Eds.), International review of industrial and organizational psychology. New York: Wiley Publishers.

Erdogan, B., & Liden, R.C. (2006). Collectivism as a moderator of responses to organizational justice: Implications for leader-member exchange and ingratiation. Journal of Organizational Behavior 27, pp. 1-17.

Gellatly, I. R. (1995). Individual and group determinants of employee absenteeism: Test of a causal model. Journal of Organizational Behavior, 16, 469–485.

Graen, G.B. and Scandura, T.A. (1987), “Toward a psychology of dyadic organizing”, Research in Organizational Behavior, Vol. 9 No. 1, pp. 175-208.

Moorman, R. H. (1991). Relationship between organizational justice and organizational citizenship behaviors: Do fairness perceptions influence employee citizenship. Journal of Applied Psychology, 76, 845–855.

Williams, E. A., Scandura, T. A., Pissaris, S., Woods, J. M., (2016). Justice perceptions, leader-member exchange, and upward influence tactics. Leadership & Organization Development Journal 37 (7), pp. 1000-1015.

How to Nudge Ethically

Author: Brian Hengesbaugh

A nudge is an intervention with the goal of changing behavior by modifying one or more factors that influence how a decision is made, while still maintaining the existing range of options and the material incentive structure for the decision maker (Thaler & Sunstein, 2008). A notification from an app on your phone, the arrangement of products in a store, the presence of a default option, and the communication of the popularity of a decision are all nudges.

Nudges are functionally similar to many forms of interventions and policies (e.g., laws, regulations, education, and fines) because they all produce, or at least seek to produce, changes in behavior. The ethical examination of nudges is distinctly different from these other forms of behavioral influence, and particularly layered, because of the manner in which the nudger (i.e., entity implementing the intervention or choice architect) exerts influence over the nudgee (i.e., individual whose behavior is being changed by the intervention) (Hausman & Welch, 2010). The key differentiating factor is transparency (Thaler & Sunstein, 2008). Nudges often modify the nonconscious elements of decision making, and can therefore operate in a way that is unseen to the people being nudged (Sleringer and Powys, 2011). This lack of transparency means that it can be difficult for nudges to be contested. As a result, nudges are prone to intentional abuse and susceptible to the unintentional incompetence of the entities performing the nudge (Hausman & Welch, 2010; Thaler & Sunstein, 2008).

Governments, advertising firms, social media platforms, and employers are just some of the agents throughout our society using nudges to exert influence over others. It is essential to create a foundation of ethical understanding of nudges in order to be able to produce “rules of engagement” for choice architects that limit abuses of power, reduce errors, and ensure that the implementation of nudges is in the service of public interest (Thaler & Sunstein, 2008). The above image presents a framework that can be used to help evaluate whether a nudge is ethical. Does the nudge promote the autonomy, safety, or welfare of the nudgee? Are the purpose, incentives, mechanisms, and qualities aligned in their promotion of autonomy, safety, and welfare of the nudgee?

Note: Special thanks to Dr. Jason Borenstein for his feedback on this frameworkFurther Reading: 

Hausman, D. M. and Welch, B. (2010), Debate: To Nudge or Not to Nudge. Journal of Political Philosophy, 18: 123-136.

Selinger, Evan and Whyte, Kyle Powys, Is There a Right Way to Nudge? The Practice and Ethics of Choice Architecture (July 10, 2011). Sociology Compass, Vol. 5, No. 10, pp. 923-935.

Thaler, R. & Sunstein, C. (2008) Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.

Corporate Sustainability: More than Going Green

Author: Hannah Ramil

A growing number of corporations are choosing to introduce sustainability efforts within the company. From plant-based, environmentally friendly laundry detergent to shoe brands that donate a pair for every pair purchased, more products are designed and marketed with sustainability in mind. Starbucks, for example touts fair-trade coffee beans, coffee sleeves made from recycled fibers, and are introducing strawless lids. This emerging pattern is known as corporate sustainability. Organizations invested over $8.7 trillion (more than twice the GDP of Germany, the fourth largest economy in the world) in corporate sustainability efforts in 2016 in the U.S. alone (USSIF, 2016). Nearly 12,000 organizations disclose their sustainability efforts through the Global Reporting Initiative, an international organization founded in 1997 to promote corporate sustainability.


According to Dyllick and Hockerts (2002) corporate sustainability is an organization’s efforts to not only meet the needs of current stakeholders, but also meet needs of future stakeholders. Hahn and colleagues (2018) argue that corporations can meet these divergent and potentially competing goals by embracing paradox theory. In other words, corporations who embrace corporate sustainability must recognize that they will likely have to embrace competing demands (e.g., immediate profit, long-term environmental sustainability), without trying to resolve them into a unified goal. Organizations who embrace this paradox perspective may balance their actions and initiatives to meet the separate competing demands, being clear about which initiatives are tied to which goals.

The definition of corporate sustainability continues to be debated, mainly because it is related to other constructs, like corporate social responsibility, environmental management, and sustainable development (Landrum, 2017). Schwartz and Carroll (2008) observe that corporate sustainability and other related constructs have three main pillars: generation of value for the company and society, balance of financial and non-financial interests, and corporate accountability for actions.

Corporate Sustainability“Meeting the needs of a firm’s direct and indirect stakeholders (such as shareholders, employees, clients, pressure groups, communities, etc.), without compromising its ability to meet the needs of future stakeholders” (Dyllick & Hockerts, 2001, p. 131).
Corporate Social Responsibility (CSR)“The social responsibility of business encompasses the economic, legal, ethical, and discretionary expectations that society has of organizations at a given point in time” (Carroll, 1979, p. 500).
Environmental Management“Encompasses all efforts to minimize the negative environmental impact of the firm’s products throughout their life cycle” (Klassen & McLaughlin, 1996, p. 1199).
Sustainable Development‘‘Development that meets the needs of the present without compromising the ability of future generations to meet their own needs’’ (WCED, 1987, p. 43).


Pressure on corporations to partake in corporate sustainability initiatives has increased in recent years. As a result, many corporations have attempted to incorporate these conscious practices in their business goals and strategies. Holding organizations accountable for the ways in which they conduct their business can yield potential lasting benefits for the wellbeing of the earth and societies within it.

Embracing corporate sustainability efforts can also benefit organizations. Eccles and colleagues (2014) examined the organizational characteristics and performance of 90 high sustainability companies and 90 low sustainability companies for a period of 18 years. In terms of organizational characteristics, they found that low sustainability companies are those that continue to operate under profit-maximization practices and view social and environmental responsibilities as externalities. In contrast, within high sustainability companies, the boards of directors are directly responsible for sustainability efforts, and leadership ties incentives to sustainability metrics. These companies also have deeper stakeholder engagements and are long-term oriented. Organizational performance outcomes differed. High sustainability companies significantly outperform low sustainability companies in terms of long-term performances in stock market and accounting.

Positive outcomes emerge within the organization from corporate sustainability efforts. Carmeli, Brammer, Gomes and Tarba (2017) conducted an empirical study on organizational Ethic of Care and employee involvement in sustainability-related behaviors at work. They found that an organizational culture based on compassion and care can motivate workers to partake in sustainability initiatives through workers’ affective reactions towards sustainability initiatives. The findings also imply that the Ethic of Care within the organization can lead to enhanced organizational identification. This enhanced organizational identification can amplify worker satisfaction and can drive sustainability-related behaviors.


Because of the variations of understanding in corporate sustainability, efforts remain inconsistent. This inconsistency makes the actual effectiveness of sustainability practices on reducing environmental damage is questionable. There is a lack of a unified understanding of corporate sustainability. To ensure that these sustainability efforts are truly meaningful, Landrum (2017) urges for a narrower conceptualization of corporate sustainability that does not prioritize profit maximization.

Further exploration on integrating corporate sustainability practices, and such integration’s effects on organizational culture constitute a key next direction in research. Linnenluecke and Griffiths (2010) suggest that the culture of a business is an important aspect to examine. Because an organization’s culture reflects organizational values, the integration of sustainability initiatives can fail if the culture is not conducive to such values. The study by Carmeli and colleagues (2017) presents a promising possibility for a type of organizational culture that is conducive to sustainability practices. However, further examination on the Ethic of Care and corporate sustainability is necessary to validate their methods. Along with Linnenlucke and Griffiths, they also suggest further examinations on different organizational cultures and how it affects sustainability efforts.

Similarly, Avota, McFadzean, and Peiseniece (2015) directed their attention to how the interplay of organizational values and personal values can influence behavior and sustainability initiatives. Their conceptual model suggests that the congruence or incongruence between organizational values and individual values influence behaviors at all levels, including individual, group, and structural behaviors, as well as management processes. The outcomes from these interplaying factors can lead to sustainability initiatives in the economic, environmental, and social dimensions. Further studies are necessary to validate their conceptual model in different organizational settings and within different cultures.Further Reading: 

Avota, S., McFadzean, E., & Peiseniece, L. (2015). Linking personal and organizational values and behavior to corporate sustainability: A conceptual model. Journal of Business Management, 10, 124-138.

Carmeli, A., Brammer, S., Gomes, E., & Tarba, S. Y. (2017). An organizational ethic of care and employee involvement in sustainability‐related behaviors: A social identity perspective. Journal of Organizational Behavior, 38(9), 1380-1395.

Carroll, A. B. (1979). A three-dimensional conceptual model of corporate performance. Academy of Management Review, 4(4), 497-505.

Dyllick, T., & Hockerts, K. (2002). Beyond the business case for corporate sustainability. Business Strategy and the Environment, 11(2), 130-141.

Eccles, R. G., Ioannou, I., & Serafeim, G. (2014). The impact of corporate sustainability on organizational processes and performance. Management Science, 60(11), 2835-2857.

Hahn, T., Figge, F., Pinkse, J., & Preuss, L. (2018). A paradox perspective on corporate sustainability: Descriptive, instrumental, and normative aspects. Journal of Business Ethics, 148(2), 235-248.

Hopwood, B., Mellor, M., & O’Brien, G. (2005). Sustainable development: mapping different approaches. Sustainable Development, 13(1), 38-52.

Klassen, R. D., & McLaughlin, C. P. (1996). The impact of environmental management on firm performance. Management Science, 42(8), 1199-1214.

Landrum, N. E. (2017). Stages of corporate sustainability: Integrating the strong sustainability worldview. Organization & Environment, DOI: 10.1177/1086026617717456.

Linnenluecke, M. K., & Griffiths, A. (2010). Corporate sustainability and organizational culture. Journal of World Business, 45(4), 357-366.

Schwartz, C. A. B., & Carroll, A. B. (2003). Corporate social responsibility: A three-domain approach. Business Ethics Quarterly, 13(4), 503-530.

USSIF Foundation. (2016). Report on US sustainable, responsible, and impact investing trends 2016.

Understanding Workspace

Author: Sanjana Basker

Many studies show that open offices—spaces with primarily shared workspace and no division between individual workers’ spaces—have negative effects on productivity, worker satisfaction, and worker health (e.g., Brennan et al., 2002; Bodin Danielsson, Chungkham, Wulff, & Westerlund, 2014; Oldham & Brass 1979). Yet, companies continue to build or redesign their workspaces within this framework. Why? How did the open office become popular? Are there alternatives to this style?

History of Physical Workspace Studies

Stepping back to the rise of factories, which emerged in the 1800s alongside the Industrial Revolution, workspaces primarily served utilitarian purposes. Factories were often unattractive with dirty halls packed with underpaid and overworked laborers (Sundstrom & Sundstrom, 1986). During World War II, workforce shortages encouraged companies to create working environments that attracted, not just housed, employees.

Prior to the 1950s, there was some examination of the way that workspaces like the traditional office building communicated messages to others. In the British Civil Service, for example, the amount of space in one’s office was correlated to one’s rank or stature. The larger the office and the more lavish the furniture, the more important the individual in the office (Baldry, 1997). In the 1950s, literature on the psychology of motivation and productivity emerged, influencing the way people thought about workspaces. By the end of the 20th century, the understanding that workplace design impacts work performance became evident, and a field of study was born (Sundstrom & Sundstrom, 1986).

Basics of Workspace Design

Psychological studies of physical workspace typically focus on three components: physical stimuli, physical structure, and symbolic artifacts. Physical stimuli are the aspects of a workspace (e.g., sights, sounds, smells) that elicit a response from someone. For example, the ringing of a phone draws attention to a call, or a screen lighting up draws attention to the computer or phone. Physical stimuli can also be more passive, such as the lighting of a room. In an open office plan, the individuals are exposed to more physical stimuli than in a traditional office layout (Smith-Jackson & Klein, 2009). Physical structure refers to the actual orientation and size of the workspace, including for example floor plan and square footage. Symbolic artifacts refer to the qualities of workspace furniture and décor that communicate messages to employees or outsiders (Davis, 1984). Such artifacts can communicate professional image, task effectiveness, status, or aesthetic cues to participants in the environment. For example, Parisian office workers at Crozier interpreted their lackluster surroundings as a lack of attention paid to them by executives in their organization (Baldry, 1997). Open office plans may communicate organizational values of collaboration, equality, or cost-savings depending on the use of symbolic artifacts and the nature of the physical structure.

The Open Office

The open-plan workspace became popular in the 1960s. (Sundstrom & Sundstrom, 1986). This plan was initially favored most by managers who were able to more easily observe all subordinates, and by executives who were able to fit more employees into a space at a time and therefore maximize space utility. However, open-plan workspaces were often unwelcome by employees. Lost in a sea of coworkers, employees reported reduced ownership in their environment and a sense of their individual importance to the organization. Studies conducted in the 1980s on the impact of these open-plan workspaces found that, beyond the lack of ownership, open-plan workspaces could be distracting and stressful for employees. The repeated distraction, from employee conversations and other physical stimuli, could result in employees seeking to distance themselves from their environments and ultimately lead to some withdrawal from the workspace (Lee & Brand 2005).

As team-based work becomes increasingly common, these shared spaces with many distractions have often been viewed as beneficial for modern collaborative projects and face-to-face communication (Smith-Jackson & Klein, 2009). Yet, empirical studies suggest that in-person communication and productivity both decline in an open office (Bernstein & Turban, 2018) and the additional distractions lead to slower work completion and increased perceptions of workload (Smith-Jackson & Klein, 2009).

Teleworking Alternatives

Telework—working remotely using the internet—is a flexible work arrangement designed to provide employees with more autonomy over how they work. Many employees appreciate the flexibility associated with being able to work remotely, or being able to only come into a physical office when necessary (Bailey & Kurland, 2002). Meta-analytic results suggest that telework improves perceived autonomy, job satisfaction, and performance, while reducing stress and work-family conflict (Gajendran & Harrison, 2007). High levels of telecommuting, however, were associated with reduced relationship quality with one’s coworkers. As such, it may still be important for remote workers to have a physical office to come to in order to interact with coworkers, supervisors, and clients.

Recently, many organizations are relying upon non-territorial workspaces to provide the amenities of a traditional office, while still cutting down on costs. The use of non-territorial workspaces also called hoteling or hot desking removes any individual ownership over a specific location within the office; workers simply use whatever location is available when they need it (Millward, Haslam, & Postmes, 2007). For teleworkers, non-territorial workspaces provide a central location to meet coworkers, supervisors, and clients when necessary. This arrangement may not have the same negative impact on satisfaction as traditional open office plans with assigned desks but may still pose problems in the form of an inability to find colleagues or an open desk (Kim, Candido, Thomas, & de Dear, 2016). On a psychological level, hot desking may be associated with a shift in employees’ identification, such that individuals who hot desk tend to identify with the organization more than with their work teams, unlike traditional employees who identify with their teams more than their organization (Millward et al., 2007).

Hotels, coffee shops, and a wide range of coworking spaces offer teleworkers or self-employed individuals opportunities to work in a third place (Moriset, 2014), a place where people congregate that is neither one’s home nor a traditional office. Coworking spaces may offer workers an opportunity to more easily network and collaborate with professionals outside of their own organization (Capdevila, 2013). Data suggest that a majority of coworking space users are freelancers or entrepreneurs (Waters-Lynch et al., 2016). About 14% of coworking space users between 2010 and 2012 were employed by organizations (Waters-Lynch et al., 2016). Third places, then, may still hold great potential for organizations who want to increase their teleworking population, while reducing or eliminating their physical office space. A recent literature review, however, suggests that employees must expend effort to plan where they will work in order to ensure the physical and social environments facilitate their work tasks (e.g., access to internet, limited distractions; Ng, 2016), thus returning to some of the same issues as open office concepts.


Open-plan offices have waxed and waned in popularity and seem to be particularly popular in the modern economy. Telework, and subsequently non-territorial workspaces and coworking spaces offer alternatives for organizations and employees to the open office plan. Certainly, much more empirical research is needed regarding these newer alternative work arrangements to determine how best to maximize employee productivity and wellbeing.

Workspace Takeaways

  1. Workspace design can communicate messages to external observers about status and success, but can also be designed to maximize productivity and community internally.
  2. Workspaces are comprised of three components: physical stimuli, physical structure, and symbolic artifacts.
  3. The open office plan has many drawbacks, yet continues to be popular among organizations. 
  4. Non-territorial workspaces and coworking spaces offer ways to mitigate some of the drawbacks of telework.

Further Reading: 

Bailey, D. E., & Kurland, N. B. (2002). A review of telework research: Findings, new directions, and lessons for the study of modern work. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 23(4), 383-400.

Baldry, C. (1997). The social construction of office space. International Labour Review,136(3), 365-378.

Bernstein, E. S., & Turban, S. (2018). The impact of the ‘open’workspace on human collaboration. Philosophical Transactions of the Royal Society of Biological Sciences, 373(1753), 20170239.

Bodin Danielsson, C., Chungkham, H. S., Wulff, C., & Westerlund, H. (2014). Office design’s impact on sick leave rates. Ergonomics, 57(2), 139-147.

Brennan, A., Chugh, J. S., & Kline, T. (2002). Traditional versus open office design: A longitudinal field study. Environment and Behavior, 34(3), 279-299.

Capdevila I (2013) Knowledge dynamics in localized communities: coworking spaces as microclusters. SSRN. doi: 10.2139/ssrn.2414121

Davis, T. R. (1984). The Influence of the Physical Environment in Offices. The Academy of Management Review, 9(2), 271-283.  

Elsbach, K. D. (2003). Relating Physical Environment to Self-Categorizations: Identity Threat and Affirmation in a Non-Territorial Office Space. Administrative Science Quarterly, 48(4), 622-654.  

Gajendran, R. S., & Harrison, D. A. (2007). The good, the bad, and the unknown about telecommuting: Meta-analysis of psychological mediators and individual consequences. Journal of Applied Psychology, 92(6), 1524-1541.

Haynes, B. P. (2008). The impact of office comfort on productivity. Journal of Facilities Management, 6(1), 37-51.

Kim, J., Candido, C., Thomas, L., & de Dear, R. (2016). Desk ownership in the workplace: The effect of non-territorial working on employee workplace satisfaction, perceived productivity and health. Building and Environment, 103, 203-214.

Lee, S. Y., & Brand, J. L. (2005). Effects of control over office workspace on perceptions of the work environment and work outcomes. Journal of Environmental Psychology, 25(3), 323-333.

Millward, L. J., Haslam, S. A., & Postmes, T. (2007). Putting employees in their place: The impact of hot desking on organizational and team identification. Organization Science, 18(4), 547-559.

Moriset, B. (2014). Building new places of the creative economy. The rise of coworking spaces. Paper Presented at the 2nd Geography of Innovation International Conference 2014. Utrecht.

Ng, C. F. (2016). Public spaces as workplace for mobile knowledge workers. Journal of Corporate Real Estate, 18(3), 209-223.

Oldham, G. R., & Brass, D. J. (1979). Employee reactions to an open-plan office: A naturally occurring quasi-experiment. Administrative Science Quarterly, 267-284.

Smith-Jackson, T. L., & Klein, K. W. (2009). Open-plan offices: Task performance and mental workload. Journal of Environmental Psychology, 29(2), 279-289.

Sundstrom, E. D., & Sundstrom, M. G. (1986). Work places: The psychology of the physical environment in offices and factories. Cambridge: Cambridge University Press.

Vischer, J. C. (2007). The effects of the physical environment on job performance: Towards a theoretical model of workspace stress. Stress and Health, 23(3), 175-184.

Vischer, J. C. (2008). Towards a user-centred theory of the built environment. Building Research & Information,36(3), 231-240.

Vischer, J. C. (2008). Towards an Environmental Psychology of Workspace: How People are Affected by Environments for Work. Architectural Science Review, 51(2), 97-108.

Waters-Lynch, J., Potts, J., Butcher, T., Dodson, J., & Hurley, J. (2016). Coworking: A transdisciplinary overview (Working Paper). Retrieved from https://papers.ssrn. com/sol3/papers.cfm?abstract_id52712217

Team Science: What Do We Know? What’s Next?

Author: Keaton A. Fletcher


Teams are ubiquitous in modern organizations; they are used to accomplish production, deliver healthcare, develop new products, provide customer service, execute military operations, and explore space. Increasingly, individuals are working in teams that are embedded in a more complex, networked, multiteam system. The prevalence of teams and multiteam systems in the modern workplace has spurred extensive research on the conditions, characteristics, and processes that contribute to high levels of team and multiteam system performance. In this short note, I describe key findings and their implications for building and developing effective teams.

Team Basics

Although definitions vary, work teams are typically defined as groups of two or more individuals who work together for the purpose of completing a task or organizational objective. Multi-team systems refer to teams of teams that work in coordinated fashion to accomplish complex organizational goals.

The voluminous research on teams can be usefully organized using a dynamic Input-Mediator-Outcome framework (e.g., Ilgen, Hollenbeck, Johnson, & Jundt, 2005). Inputs refer to the team member attributes (e.g., personality, skills, knowledge, preferences, etc.), task demands, available resources, and characteristics of the environment or context (e.g., supportive organization, isolated confined environment) available to the team. These inputs in turn affect the conditions in which team processes develop and unfold. During the process phase, teams develop shared knowledge and thoughts, experience different affect and motivational states, and develop strategies for accomplishing the task and managing their environment. Together, these processes and emergent states, collectively referred to as mediators, convert team inputs to outputs. Outputs include literal task outputs, like widgets, marketing campaigns, or research ideas; but can also include attitudinal and cognitive outcomes such as job and team satisfaction or learning. Moving into the next performance cycle, then, these outputs act as inputs, determining the new starting conditions for the team.

Input-Mediator-Outcome model of teams.

Team Inputs

In the modern workplace, two inputs have received the greatest research attention: Who is in the team, and what is the context in which the team works? In a review paper, Goodwin and colleagues (2018) highlight findings that confirm the value of well-staffed teams: such teams perform better than would have been expected by simply adding together the individual’s contributions. This finding runs contrary to previous work (e.g., Bedwell et al, 2012) suggesting that teams lose productivity due to the additional coordination and communication demands, thus emphasizing the value of starting with the right people. One important aspect of team inputs concerns team member diversity. Although diversity is often thought of in terms of surface-level characteristics, such as sex, age, or race, findings by Bell and colleagues (2018) indicate that such characteristics have a less powerful impact on team performance than diversity of deep-level characteristics, such as values, experiences, and personality. In a related vein, teams often need members who bring different knowledge or competencies to the table, ensuring that the team can complete complex and dynamic tasks (Mathieu, Wolfson, & Park, 2018). Recognizing this, for example, a project sponsored by the U.S. Army developed a team optimal profiling system, to match individual abilities and traits to teams where the individual is most needed and would best fit (Donsbach et al., 2009).

The context in which a team works represents a second major input to team function and effectiveness. Context is a broad term that refers to the physical, psychological, social, and task factors that make up the environment in which the team works. In the healthcare setting for example, work teams in intensive care units tend to change membership frequently and often operate in stressful environments that are not physically conducive to strong teamwork (Ervin, Kahn, Cohen, & Weingart, 2018). However, primary care teams are typically characterized by relatively stable membership, routinized teamwork, and (compared to ICU teams) less exposure to death and dying (Fiscell & McDaniel, 2018). Yet both ICU teams and primary care teams, are typically comprised of individuals from a variety of disciplines and professions within medicine and healthcare. Further, the team context may also differ as a function of industry or task. For example, research teams who need to innovate and collaborate, often across great distances (Hall et al., 2018), differ greatly from astronaut teams who must work in isolated confined environments (e.g., Landon, Slack, & Barrett, 2018), or disaster response teams who need to coordinate with multiple other interprofessional teams in rapidly changing and often ambiguous settings (e.g., Power, 2018). Identifying the key resources and constraints of the context in which the team operates in tandem with team composition to affect team effectiveness and performance.

Team Processes and Emergent States

Team inputs alone rarely univocally determine team performance. Teams operate over space and time, allowing for the development of processes and emergent psychological states that mediate the input-outcome relationship. Teams engage in two types of behaviors: taskwork and teamwork. Taskwork is any behavior that focuses on the task itself (e.g., goal setting, performance, situation analysis) while teamwork is any behavior that focuses on the interpersonal relationships within the team (e.g., emotion and motivation management, conflict management; Marks et al., 2001). Although teamwork occurs at all points in a team’s life, taskwork is further differentiated into transition and action periods. Specifically, taskwork often occurs in cycles that begin with transition periods—times in which the team evaluates past performance and plans for future performance—and then shift into action periods—times in which the team executes their plans.

Researchers and practitioners must also move beyond understanding what teams do to begin understanding what team members think and feel. These emergent states arise within the team due to interpersonal interactions (Kozlowski & Chao, 2018). For example, as team members work together they develop a shared understanding of the task and environment, as well as a working understanding of who knows what within the team. These two cognitive emergent states, known as shared mental models and transactive memory systems, respectively, capture the development of common knowledge across team members over time. Shared mental models and transactive memory develop in a continuous fashion and change as the team continues to interact, and degrade when the team disbands. Another example of an emergent state is psychological safety (Edmondson, 1999)—a team climate in which individual members feel comfortable expressing their opinions and dissent without fear of ridicule or repercussions. In modern organizations which are increasingly flatter, and increasingly global (meaning team members may come from cultures with different levels of comfort with conflict and power distance), understanding how to create a strong sense of psychological safety is particularly important. Research findings suggest that higher levels of transformational leadership, interdependence among team members, role clarity, and support from peers can improve levels of psychological safety within teams (Frazier et al., 2017), further implying that interventions designed to highlight each individual’s unique role and how it fits within the team, while also selecting and training team members and leaders to improve levels of social support, can improve psychological safety, and ultimately team outcomes.

Team Outcomes

The most salient team outcome is team performance on some organizationally-valued criterion (e.g., number of homes sold, patients served, etc.). However, because teams operate over time and produce outcomes repeatedly over the life of the team, we must move beyond team performance to consider how teams affect the knowledge/beliefs, affective states, and health outcomes of team members. Much work has been done examining how teams can better learn from their experiences and performance episodes, through interventions like debriefing (Lacerenza, Marlow, Tannenbaum, & Salas, 2018). It is also important that team members experience a sense of satisfaction with, and commitment to their teammates, the team as a whole, and the organization (e.g., Kirkman & Shapiro, 2001). Although evidence is beginning to accumulate on how teams influence affect and work attitudes, relatively less is known about how team processes influence worker health outcomes (e.g., cardiovascular disease, stress, sleep). In the modern, increasingly health-conscious, world, it is imperative that researchers begin to understand these connections.

Looking Forward

The ubiquity of teams is changing the human experience of work. Multi-team systems, in which work is accomplished by teams of teams organized in a variety of structures, places new demands on employees charged with coordinating their work within a team with progress made in other teams. In many cases, individuals may also belong to multiple teams. These developments are challenging scientists and practitioners to think differently about teams, and to address new questions related to the structures and processes that promote communication and coordination between teams while at the same time sustaining a strong culture of psychological safety, inter-team collaboration, and productivity.

The costs of failure to understand and identify the processes and obstacles involved in multi-team systems is vividly exemplified by experience of the Jet Propulsion Laboratory and Lockheed Martin Astronautics that sought to launch a $125-million orbiter to Mars. One team provided all calculations in metric units, while the other provided all calculations in the English system, resulting in a miscalculation that led to the disintegration of the probe in the atmosphere of Mars in 1998. Had either of these teams been working in isolation, this communication error would not have been an issue, but the project likely could not have succeeded. Had the multi-team system been better designed with a stronger culture, or understanding of the inputs, processes, and outcomes between and within the teams, perhaps this issue would have been mitigated. As the Mars orbiter example demonstrates, research to aid the design and identification of key processes in the development of effective multi-team systems has practical implications for the individual, the organization, and society.

Team Takeaways

  1. Team composition is more than skin deep. Surface level characteristics (e.g., gender) are generally not as important as deeper characteristics (e.g., attitudes) in effective teams.
  2. Context matters. There is no single most effective team structure; effective teams are those which can respond and adapt to the demands of their environment.
  3. Teams are more than the sum of their parts. Effective teams transform inputs to valued work outcomes through different processes and emergent psychological states that occur during action.
  4. Team membership is an experience that provides individuals with a lens through which events, interactions, and behaviors are understood. Team membership affects what team members know and how team members think (attitudes), feel (affect), and behave. These features work in unison to influence team performance.
  5. Teams change over time as a function of the events, interactions, and competencies that develop and occur within and across teams.

Further Reading: 

Bedwell, W. L., Wildman, J. L., DiazGranados, D., Salazar, M., Kramer, W. S., & Salas, E. (2012). Collaboration at work: An integrative multilevel conceptualization. Human Resource Management Review, 22(2), 128-145.

Bell, S. T., Brown, S. G., Colaneri, A., & Outland, N. (2018). Team composition and the abcs of teamwork. American Psychologist, 73(4), 349-362.

Donsbach, J. S., Tannenbaum, S. I,. Mathieu, J. E., Salas, E., Goodwin, G. F., & Metcalf, K. A. (2009). Team composition optimization: The team optimal profile system (TOPS; Tech. Rep. No. 1249). Arlington, VA: U. S. Army Research Institute.

Driskell, J. E., Salas, E., & Driskell, T. (2018). Foundations of teamwork and collaboration. American Psychologist, 73(4), 334-348.

Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350-383.

Ervin, J. N., Kahn, J. M., Cohen, T. R., & Weingart, L. R. (2018). Teamwork in the intensive care unit. American Psychologist, 73(4), 468-477.

Fiscella, K., & McDaniel, S. H. (2018). The complexity, diversity, and science of primary care teams. American Psychologist, 73(4), 451-467.

Frazier, M. L., Fainschmidt, S., Klinger, R. L., Pezeshkan, A., & Vracheva, V. (2017). Psychological safety: A meta-analytic review and extension. Personnel Psychology, 70, 113-165.

Goodwin, G. F., Blacksmith, N., & Coats, M. R. (2018). The science of teams in the military: Contributions from over 60 years of research. American Psychologist, 73(4), 322-333.

Hackman, J. R., & Wageman, R. (2005). A theory of team coaching. The Academy of Management Review, 30(2), 269-287.

Hall, K. L., Vogel, A. L., Huang, G. C., … Fiore, S. M. (2018). The science of team science: A review of the empirical evidence and research gaps on collaboration in science. American Psychologist, 73(4), 532-548.

Ilgen, D. R., Hollenbeck, J. R., Johnson, M., & Jundt, D. (2005). Teams in organizations: From input-process-output models to IMOI models. Annual Review of Psychology, 56, 517-543.

Kaplan, M., Dollar, B., Melian, V., Van Durme, Y., & Wong, J. (2016). Human capital trends 2016 survey. Oakland, CA: Deloitte University Press. Retrieved from

Kirkman, B. L., & Shapiro, D. L. (2001). The impact of cultural values on job satisfaction and organizational commitment in self-managing work teams: The mediating role of employee resistance. Academy of Management Journal, 44(3), 557-569.

Kozlowski, S. W. J., & Chao, G. T. (2018). Unpacking team process dynamics and emergent phenomena: Challenges, conceptual advances, and innovative methods. American Psychologist,73(4), 576-592.

Lacerenza, C. N., Marlow, S. L., Tannenbaum, S. L., & Salas, E. (2018). Team development interventions: Evidence-based approaches for improving teamwork. American Psychologist, 73(4), 517-531.

Landon, L. B., Slack, K. J., & Barrett, J.D. (2018). Teamwork and collaboration in long-duration space missions: Going to extremes. American Psychologist, 73(4), 563-575.

Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and taxonomy of team processes. Academy of Management Review, 26(3), 356-376.

Mathieu, J. E., Hollenbeck, J. R., van Knippenberg, D., & Ilgen, D. R. (2017). A century of work teams in the Journal of Applied Psychology. Journal of Applied Psychology, 102(3), 452-467.

Mathieu, J. E., Wolfson, M. A., & Park, S. (2018). The evolution of work team research since hawthorne. American Psychologist, 73(4), 308-321 Power, N. (2018). Extreme teams: Toward a greater understanding of multiagency teamwork during major emergencies and disasters. American Psychologist, 73(4), 478-490. 

Rosen, M. A., DiazGranados, D., Dietz, A. S., Benishek, L. E., Thompson, D., Pronovost, P. J., & Weaver, S. J. (2018). Teamwork in healthcare: Key discoveries enabling safer, high-quality care. American Psychologist,73(4), 433-450.

Salas, E., Reyes, D. L., & McDaniel, S. H. (2018). The science of teamwork: Progress, reflections, and the road ahead, American Psychologist, 73(4), 593-600.

Shuffler, M. L., & Carter, D. R. (2018). Teamwork situated in multiteam systems: Key lessons learned and future opportunities. American Psychologist, 73(4), 390-406.

Thayer, A. L., Petruzelli, A., & McClurg, C. E. (2018). Addressing the paradox of the team innovation process: A review an practical considerations. American Psychologist, 73(4), 363-375.

Science for Good: Humanitarian Work Psychology

Author: Brian Hengesbaugh

Defining and Establishing Humanitarian Work Psychology

Humanitarian Work Psychology (HWP) is theapplication of the scientific principles of psychology to the context of work with the deliberate goal of enhancing individual human welfare (Clayton and Foster, 2013). HWP was first established in 2009 when a group of Industrial-Organizational (I-O) Psychologists met in London to discuss their common interest in using work to improve outcomes for underserved populations (Stringer, 2016). As a subdiscipline of I-O Psychology, HWP represents the intentional expansion of the scope of psychological sciences to include data-driven practices for improving living and working conditions for people across the globe (Clayton and Foster, 2013).

How is HWP Related to Other Work Psychology Domains?

Because work provides opportunities for individuals to interact directly with the social, political, and economic factors that shape their lives (Blustein, 2008), it holds unique power as a humanitarian tool. HWP therefore draws from a variety of established psychology domains that examine work.

Psychology FieldTopics of InterestHWP Example
I-OMotivating factors Worker and task selectionTraining and performance managementWorker productivity and organizational effectiveness (Blustein, 2008)Using results of personality profiles as criteria for bank loans for women who would otherwise be denied due to lack of formal financial documentation (Klinger, 2011)
VocationalCareer and work-based decision makingIndividual strengths Interaction with the environment (Blustein, 2008)Investigation of the impact of marginalization and exclusion on immigrant workers seeking employment in a new job market (Maynard et al., 2010).
Occupational HealthEmployee healthSafetyWell-beingOccupational stressorsWorkers’ families’ outcomes (Ahmed, 2017)Understanding the nature of stressors, coping mechanisms, and psychological wellbeing in humanitarian workers in Colombia (Vergara & Gardner, 2011)
PositiveHuman strengths and virtuesOptimal functioning of individuals, groups, and organizationsMeaningful life (Gable & Haidt, 2005)The role psychological capital plays in explaining the relationship between decent work and motivation at work. (Ferraro et al., 2017)

HWP is similar to these domains of psychology in that they are all evidence-based, interdisciplinary fields concerned with understanding the interplay of psychological, social, and organizational factors in improved outcomes for individuals and organizations (Ahmed, 2017). But unlike other domains, HWP primarily focuses on expanding access to decent work, especially for those living in poverty or in developing countries, in an effort to specifically achieve humanitarian outcomes (Clayton & Foster, 2013).

Understanding Decent Work

At the core of HWP is the notion of decent work (Carr & Thompson, 2013). Decent work supports the economic, psychological, and physical health of workers, families, and communities (Duffy et al., 2016). It provides fair income, secure work environments, opportunities for growth, freedom to express concerns, and the ability to contribute to decisions that affect workers’ quality of life (Duffy, Blustein, Diemer, & Autin, 2016). The primary outcome of securing decent work is the fulfillment of three individual needs: survival, social connection, and self-determination (Duffy et al., 2016). The attainment of these needs through decent work creates a foundation upon which people can experience job fulfillment and individual well-being (Duffy et al., 2016).

Marginalization, however, creates significant obstacles to accessing decent work (Duffy et al., 2016). For example, students in poverty and students of color report feeling less connected to academic institutions than their peers, which is a contributing factor to diminished work outcomes (Duffy et al., 2016). Further, workers with physical disabilities reported that their co-workers’ lack of knowledge regarding the severity of their disability created interpersonal barriers that were cited as being more challenging than structural barriers like climbing the stairs (Crooks, 2007).

When considering access to decent work, individual attributes such as proactive personality (i.e., propensity to take initiative) and critical conscience (i.e., ability to use moral awareness to challenge social constructs) are influential factors. In addition to the individual-level factors, HWP explores the role of sociocultural factors (e.g., language, education, attitudes, family structure, etc. ) in work related decisions and the work experience for all people, particularly those who are marginalized on the basis of race, social class, disability, and/or gender. To understand the psychological nature of work, we need to study these sociocultural factors, especially as they relate to the context in which marginalized people experience work (Duffy et al., 2016).

The Growth of HWP

HWP has grown in concert with the rising global emphasis on addressing the needs of underserved and marginalized populations, as outlined in the United Nations’ Millennium Development Goals released in 2000. Economic circumstances such as the Great Recession, in conjunction with the growing influence of technology, also played a role in the establishment of HWP (Duffy et al., 2016). These economic factors produced a diminished job market that disproportionately affected individuals lacking high level professional skills (Duffy et al., 2016). This led to heightened concentrations of income and wealth among the upper decile of the population and further reduced the socioeconomic power of the poor (Duffy et al., 2016), thus highlighting the need for systemic changes to lift underserved populations out of poverty.

In 2008, the ILO developed a framework of Decent Work Indicators used to measure the growth of decent work within an economy (International Labour Organisation, 2013). The measurement framework is built on four strategic pillars: full and productive employment, rights at work, social protection, and the promotion of social dialogue (International Labour Organisation, 2013). The ILO asserts that countries should use the measurement framework as a starting point for monitoring decent work, and that the statistical indicators of decent work are expected to change as research in this field continues (International Labour Organisation, 2013).


Overall, the nascent field of humanitarian work psychology reflects global and local workplace and social trends. By applying the psychological, social, and organizational concepts of existing work psychology domains to the Millennium Development Goals through the focus on access to decent work for all people, HWP can help researchers and practitioners better understand the social, economic, and individual factors associated with the working experience of impoverished and marginalized individuals.

Practical HWP Takeaways:

  1. Promote decent work – Employees, employers, and policy makers can seek to create work environments that meet the fundamental needs of survival, social connection, and self-determination.
  2. Measure decent work – Researchers and policy makers can adapt and utilize the International Labour Organization’s Decent Work Indicators to gather baseline data and measure the growth of decent work.
  3. Identify and address patterns of marginalization – Employees, employers, policy makers, and researchers can pursue an understanding of: the sociocultural factors that are specific to communities, how these factors may be leading to employment marginalization, and interventions that will reduce employment marginalization along sociocultural lines.
  4. >Contribute to HWP research – Make connections with universities, companies, labor organizations, and government offices to learn how to help develop the field of HWP and enhance human welfare through access to decent work.

Further Reading: 

Ahmed, S. (2017, October). Humanitarian Work Psychology and Occupational Health Psychology: Two sides of the same coin? Global Organisation for Humanitarian Work Psychology Newsletter. Retrieved from

Blustein, D. L. (2008). The Role of Work in Psychological Health and Wellbeing: A Conceptual, Historical, and Public Policy Perspective. American Psychologist, 63 (4), 228-240

Carr, S. C. & Thompson, L. (2013). Humanitarian Work Psychology: Concepts to Contributions. Society for Industrial and Organizational Psychology, Inc. (2013), 24

Clayton, A. & Foster, L. (2013). Psychology In Action: Humanitarian work psychology at North Carolina State University’s IOTech4D Lab. Psychology International, 24 (2), 6-9

Duffy, R. D., Blustein, D. L., Diemer, M. A., & Autin, K. L. (2016). The Psychology of Working Theory. The Journal of Counseling Psychology 63 (2), 127-148

Gable, S. & Haidt, J. (2005). What (and Why) is Positive Psychology? Review of General Psychology 9 (2), 103-110

International Labour Organisation (2013). Decent Work Indicators: Guidelines for producers and users of statistical and legal framework indicators. ILO Manual, second version. Retrieved from—dgreports/—integration/documents/publication/wcms_229374.pdf

International Labour Organisation (2018). Decent Work. Retrieved from–en/index.htm

United Nations (2015). We Can End Poverty: Millennium Goals and Beyond 2015. Retrieved from

Stringer, H. (2016). Humanitarian Work Psychology: This new psychology field focuses on underserved populations. Monitor On Psychology, 47(4), 61.

The Places People Go

Author: Cathy Liu

What is Geographical Mobility?

When considering whether someone will move locations for work, a phenomenon called geographic mobility, two demographic factors are traditionally important: college graduate status and employment status. Research on geographic mobility has focused on psychological factors such as personality, and economic factors such as tenure and economic standing, that affect an individual’s willingness to move. In current geographic mobility literature, there have been three major findings: (1) younger adults move more frequently than older adults, (2) college graduates are more likely to move than non-college graduates, and (3) those who are unemployed are more willing to move for work than those who are employed, regardless of age or sex.

Younger Adults Move More Frequently than Older Adults

As employees approach their late twenties, their willingness to relocate typically increases and reaches its peak (Schachter, 2004). Some have argued that this could be because individuals in this age range are still in the process of leaving home, getting married, starting their careers, and/or having children (Fischer, 2002). As age increases past the late twenties, unwillingness to relocate for work increases. Those in the 35 – 39 age group showed the highest rate of unwillingness to relocate. As age increased into the 40s, the desire to relocate closer to home and children increased which positively affected relocation for this cohort (Chapa & Wang, 2014). Although family and personal factors have a positive influence on relocation for older employees, older employees perceive their future career opportunities as less bright in comparison to those of younger employees which causes them to be less willing to relocate for work related purposes (Brett et al., 1993).

College Graduates are More Likely to Move than Non-College Graduates

From 2002 to 2003, 11 percent of those with a high school education moved for work, compared to 13 percent of those with a bachelor’s degree. Movers with a bachelor’s degree were more likely to move longer distances: 23 percent made an interstate move in comparison to 15 percent of those with a high school education (Schachter, 2004). From a sample of both in-state and out-of-state students from the University of Pittsburgh, 33 percent of students wanted to stay in Pittsburgh after graduation, while 35 percent wanted to move to a different state, and 5 percent wanted to move to a different country. Power motivation, the desire to obtain positions of authority, strongly predicted wanting to leave the state in search for better opportunities for work (Frieze et al., 2006). Those with higher education levels are better able to obtain more information and are better equipped to process the information which reduces search and transaction costs, thus lessening the burden when choosing to relocate.

Unemployment and Geographic Mobility Choices

In general, those who are unemployed are more likely to move than those who are employed (Schachter, 2004). Specifically, the migration rate is twice as high for unemployed people (10.9%) in comparison to employed people (5.7%; Saben, 1964). Further, unemployed individuals are more likely than employed individuals to migrate to an entirely new location, with no personal connections for work (Arntz, 2005). As the amount of search time increases, the probability of migration increases as well. Individuals who have the financial resources (e.g., unemployment insurance) to sustain them on a thorough job search for better opportunities and better fit with their interests and abilities may actually be more geographically mobile (Nunn et al., 2018). 

Remaining Research Questions

Although there has been extensive research regarding geographic and job mobility, there are still many questions that have not been answered. Little attention has been paid to the relationship between where individuals are willing to move to and their personal connections with each location. For example, is an out-of-state college graduate moving back to their hometown analogous to a high school graduate taking a job in a city where they have no personal connections? Both individuals are making moves, but can they be quantified as the same type of move?

There has been little focus on the psychological well-being of those who have moved from rural to urban areas even though urbanization has increased over the past century. On the other hand, many incentive programs exist to bring professionals to rural locations, and little work has been done to examine the psychological and health impacts of this type of move.

Lastly, in our modern society, it is important to study mobility in occupations where there has been a decline in jobs due to technological innovations that have rendered some professions obsolete. Is it that individuals are more willing to move because of job scarcity, or that they are less wiling to move because they do not see a long-term advantage to staying within the same occupation?

Importance of Understanding Geographic Mobility

The role of geographic mobility and job mobility is ever more important in our fast-paced, globalized society. For hiring managers, understanding who is (or is not) likely to accept a position outside of their current location is crucial to avoid wasted resources. For policy makers, it is important to understand the societal, technological, and economic changes that are influencing individuals’ job searches and migration patterns. For workers, it is critical to recognize the value and costs associated with geographic mobility, particularly as a function of age or as a result of job loss.

Job Mobility Takeaways
  1. An individual’s willingness to move is a result of both psychological and situational factors.
  2. Younger adults are more likely to move than older adults due to differences in life events and life stage.
  3. College graduates are more likely to move than non-college graduates due to access to more information and a stronger desire to obtain positions of authority.
  4. Those who are unemployed are far more likely to uproot and migrate to new locations without any personal connections.

Further Reading: 

Arntz, M. (2005). The Geographical Mobility of Unemployed Workers. SSRN Electronic Journal. doi:10.2139/ssrn.732284

Brett, J. M., Stroh, L. K., & Reilly, A. H. (1993). Pulling up roots in the 1990s: Whos willing to relocate? Journal of Organizational Behavior,14(1), 49-60. doi:10.1002/job.4030140106

Borsch-Supan, A. (1990). The Double-edged Impact of Education on Mobility. Economics of Education Review,9(1). doi:10.3386/w2329

Chapa, O., & Wang, Y. J. (2014). Gender Role And Culture In Pre-Employment Relocation Decisions. Journal of Applied Business Research (JABR),30(4), 1109. doi:10.19030/jabr.v30i4.8658

Fischer, C. S. (2002). Ever-More Rooted Americans. City and Community,1(2), 177-198. doi:10.1111/1540-6040.00016v

Frieze, I. H., Hansen, S. B., & Boneva, B. (2006). The migrant personality and college students’ plans for geographic mobility. Journal of Environmental Psychology,26(2), 170-177. doi:10.1016/j.jenvp.2006.05.001

Nunn, R., Kawano, L., & Klemens, B. (2018). Unemployment Insurance and Worker Mobility. Tax Policy Center. Retrieved March 24, 2018.

Ratcliffe, M. (2010). A Century of Delineating a Changing Landscape: The Census Bureau’s Urban and Rural Classification, 1910 to 2010(Rep.). U.S. Census Bureau.

Saben, S. (1964). Geographic Mobility and Employment Status, March 1962—March 1963. Monthly Labor Review,87(8), 873-881. Retrieved March 24, 2018.

Schachter, J. P. (2004, March). Geographical Mobility: 2002 to 2003. Current Population Reports.

Being Mindful About Mindfulness

Author: Jacqueline Jung

What is Mindfulness?

Mindfulness soared into popularity in the 2000s and has since become a topic of interest in nearly every domain of psychology, and an influential practice for a considerable subset of the public (Brown & Ryan, 2003). Mindfulness is generally defined as a state of heightened, intentional, non-judgmental awareness (Kabat-Zinn, 2003). Three components of mindfulness—intention, attention, and attitude—have been identified, capturing the motivation, cognitive processes, and affective responses associated with mindful states (Shapiro et al., 2006). Although some studies examine mindfulness as a more trait-like construct (e.g., Siegling, 2014) this conceptualization has less value for researchers and practitioners interested in improving mindfulness skills.  As such, mindfulness is often measured as a mental state characterized by full attention to present-moment experience.

Correlates of Trait Mindfulness

Trait mindfulness, the relatively stable disposition to be more or less mindful, has been shown to help bolster the effects of mindfulness-based interventions (MBIs; Shapiro et al., 2011). In the occupational domain, employer trait mindfulness has been linked to follower job satisfaction, job performance, and well-being (Reb et al., 2014). Trait mindfulness has also been associated with greater work-family balance (Allen & Kiburz, 2012), healthier eating, including fewer impulsive choices, healthier snack decisions, and reduced caloric consumption (Jordan et al, 2014). Trait mindfulness has also been shown as a weak, negative predictor of substance abuse behaviors, primarily alcohol and tobacco use (Karyadi et al., 2014).

Mindfulness-Based Interventions

Relatively large-scale attempts to manipulate mindfulness have been gaining traction in the applied domain. For example, mindfulness is employed in Google’s business practices, is a standard psychotherapy of the National Health Service in the U.K. and is being tested as part of the standard education for about 6,000 children aged 11 to 14 across seventy-six different schools in London (Kuyken, 2017). Mindfulness-based training is also being tested for U.S. military resilience; it has recently been used among U.S. Marines prior to deploying to Iraq in order to buffer against the stressors of military deployment (Stanley et al., 2011). A review of mindfulness-based interventions in occupational settings examined 153 published papers with 12,571 participants and found that mindfulness was generally associated with positive outcomes in measures of mental health, well-being, and job performance (Lomas et al., 2017). Sixty-four of these studies presented data from MBIs, of which the authors identified only 21 that provided high quality descriptions of their studies. In general, these studies supported the positive effects of MBIs on reported mindfulness and a range of outcomes such as anxiety, stress, anger, sleep, job satisfaction, resilience, and job performance. A qualitative review of workplace MBIs (Eby et al., in press) suggested the main outcome of interest in these studies tends to be effects on  stress/strain.

Areas for Improvement in Mindfulness Research

Despite the promise mindfulness holds, Van Dam and colleagues (2018) have recently suggested that methodological issues common to studies of mindfulness may lead the public to be disappointed or misled. First, the authors argued that because mindfulness is currently being used as an umbrella term to describe a large number of practices, processes, and characteristics focusing on attention, awareness, memory, and acceptance, there is no single agreed upon definition of the construct.  Similarly, Lomas and colleagues (2017), as well as Eby and colleagues (in press), also highlighted the variability in the content, delivery, evaluation, and reporting of mindfulness-based interventions. This may contribute to inconsistent results since some researchers may focus upon state or trait mindfulness while others examine mindfulness skills and practices. Van Dam and colleagues (2018) also point toward challenges to clinical interventions, such as haphazard variability across mindfulness-based interventions and misperceptions of therapeutic efficacy. They also draw attention to potential adverse effects from practicing mindfulness, including over twenty published cases of meditation-related psychosis, mania, depersonalization, anxiety, panic, and traumatic memory re-experiencing. Another thing to keep in mind is that exaggerated benefits of mindfulness may potentially divert patients from pursuing activities such as aerobic exercise or standard treatments (e.g. psychotherapy). Lastly, Van Dam and colleagues call into question interpretations of mindfulness data due to limitations in neuroimaging and problematic analyses of brain activity depictions.

It is not all bad news for mindfulness, though. In response to Van Dam and colleagues’ (2018) review, Davidson and Dahl (2018) suggested that Van Dam and colleagues’ primary focus on clinical outcomes (such as depression and addiction) may be missing critical, meaningful, and potentially more potent effects on non-clinical outcomes (e.g., life satisfaction, job satisfaction, motivation). Davidson and Dahl also suggest that much of the variability in mindfulness results may be due to variability in the duration and intensity of mindfulness practice, giving rise to many theoretical and practical questions. For example, is it more useful to practice meditation in brief but multiple sessions in a given day, or is it more beneficial to have one long session? Is daily practice more or less impactful than periods of intensive practice such as retreats? Davidson and Dahl also argue that mobile technology may enable the standardization and collection of large datasets on mindfulness interventions.

Five Mindfulness Takeaways
  1. Mindfulness has been viewed as a multidimensional trait, state, or skill set, depending on context.
  2. Generally, studies of mindfulness-based interventions in occupational settings show a range of positive impacts, especially on stress/strain.
  3. Because there is no standard definition, operationalizing mindfulness becomes difficult, which then leads to methodological issues and inconsistent results.
  4. Considering mindfulness-based interventions, there is no standard format, content, delivery, or reporting system. This may explain inconsistent results.
  5. Overall, more consistency in defining, manipulating, and evaluating mindfulness may help researchers and practitioners alike in understanding the relationship between mindfulness and outcomes of interest.

Further Reading: 

Allen, T. D., & Kiburz, K. M. (2012). Trait mindfulness and work-family balance among working parents: the mediating effects of vitality and sleep quality. Journal of Vocational Behavior, 80, 372–379.

Brown, K. W., & Ryan, R. M. (2003). The benefits of being present: Mindfulness and its role in psychological well-being. Journal of Personality and Social Psychology84, 822–848.

Davidson, R. J., & Dahl, C. (2018). Outstanding challenges in scientific research on mindfulness and meditation. Perspectives on Psychological Science13, 62–65. 

Fresco, D. M., Moore, M. T., van Dulmen, M. H. M., Segal, Z. V., Ma, S. H., Teasdale, J. D., & Williams, J. M. G. (2007). Initial psychometric properties of the experiences questionnaire: Validation of a self-report measure of decenter-ing. Behavior Therapy38, 234–246.

Hugh-Jones, S., Rose, S., Koutsopoulou, G.Z. et al. Mindfulness (2017).

Kabat-Zinn, J. (2003). Mindfulness-based interventions in context: past, present, and future. Clinical Psychology: Science and Practice, 10, 144–156.

Karyadi, K. A., VanderVeen, J. D., Cyders, M. A. (2014). A meta-analysis of the relationship between trait mindfulness and substance use behaviors. Drug and Alcohol Dependence143, 1–10.

Kuyken, W., Nuthall, E., Byford, S., Crane, C., Dalgleish, T., Ford, T., … & Williams, J. M. G. (2017). The effectiveness and cost-effectiveness of a mindfulness training programme in schools compared with normal school provision (MYRIAD): study protocol for a randomised controlled trial. Trials, 18, 194.

Lomas, T., Medina, J. C., Ivtzan, I., Rupprecht, S., Hart, R. Eiroa-Orosa, F. J. (2017). The impact of mindfulness on well-being and performance in the workplace: an inclusive systematic review of the empirical literature. European Journal of Work and Organizational Psychology26, 492–513.

Jordan, C. H., Wang, W., Donatoni, L., Meier, B. P. (2014). Mindful eating: trait and state mindfulness predict healthier eating behavior. Personality and Individual Differences68, 107–111.

Reb, J., Narayanan, J. Chaturvedi, S. Mindfulness (2014). 5: 36.

Shapiro, S. L., Carlson, L. E., Astin, J. A., Freedman, B. (2006). Mechanisms of Mindfulness. Journal of Clinical Psychology, 62, 373–386.

Stanley, E. A., Schaldach, J. M., Kiyonaga, A., Jha, A. P. (2011). Mindfulness-based mind fitness training: a case study of a high-stress predeployment military cohort. Cognitive and Behavioral Practice, 18, 566–576.

Van Dam, N. T., van Vugt, M. K., Vago, D. R., Schmalzl, L., Saron, C. D., Olendzki, A., Meyer, D. E. (2018). Mind the hype: A critical evaluation and prescriptive agenda for research on mindfulness and meditation. Perspectives on Psychological Science13, 36–61.

Van Dam, N. T., van Vugt, M. K., Vago, D. R., Schmalzl, L., Saron, C. D., Olendzki, A., Meyer, D. E. (2018). Reiterated concerns and further challenges for mindfulness and meditation research: A reply to Davidson and Dahl. Perspectives on Psychological Science13, 66–69.

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