Author: vpham30

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
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. 

Robots
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 et.al, 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. https://roboticsandautomationnews.com/2017/03/04/bmw-shows-off-its-smart-factory-technologies-at-its-plants-worldwide/11696/ 

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: 
https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Future%20of%20Organizations/What%20the%20future%20of%20work%20will%20mean%20for%20jobs%20skills%20and%20wages/MGI-Jobs-Lost-Jobs-Gained-Report-December-6-2017.ashx 

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), 
151-181.

How Human-Robot Teamwork Will Upend Manufacturing (2014, Sept 16). Retrieved from 
https://www.technologyreview.com/s/530696/how-human-robot-teamwork-will-upend-manufacturing/

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.
doi:10.1145/3173087

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 
News. https://www.greenvilleonline.com/story/news/2018/01/31/bmw-doubles-down-tech-apprentice-program/1082773001/ 

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. 
doi:10.1016/0066-4138(69)90004-4

Sennaar, K. (2018). Machine learning for medical diagnostics — 4 current applications. Techemergence. https://www.techemergence.com/machine-learning-medical-diagnostics-4-current-applications/ 

Shahin, M. A. (2016). State-of-the-art review of some artificial intelligence applications in pile
    foundations doi:https://doi.org/10.1016/j.gsf.2014.10.002

The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning.
(2017, December 4). Retrieved from
https://medium.com/iotforall/the-difference-between-artificial-intelligence-machine-learning-and-deep-learning-3aa67bff5991

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. https://aws.amazon.com/blogs/aws/launch-announcing-alexa-for-business-using-amazon-alexas-voice-enabled-devices-for-workplaces/ 

World Economic Forum（2016), The Future of Jobs: Employment Skills and Workforce
Strategy for the Fourth Industrial Revolution. Retrieved from World Economic Forum website: http://www3.weforum.org/docs/WEF_Future_of_Jobs.pdf. 

What is Cloud Computing: a beginner’s guide. (2018, January 28). Retrieved from 
https://azure.microsoft.com/en-us/overview/what-is-cloud-computing/

Who Needs the Internet of Things? (2016, September 13). Retrieved from
https://www.linux.com/news/who-needs-internet-things 

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
ICML.

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. 

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. 

References
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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
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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.

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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.