An engaged workforce contributes to higher productivity, lower turnover, and a more successful organization. This document discusses employee engagement and key organizational efforts that lead to an engaged workforce. Defining Engagement To fully understand employee engagement, it should be approached from both an operational and analytical standpoint. Avatar Solutions defines engagement as an employee who is invested in and enthusiastic about their work and also acts as a proponent of their employer. While job satisfaction is a key element of this, it is not the only part of engagement any employee can be perfectly content to clock in, do the minimum amount of work required of them for the day, and clock out. Instead of relying solely on measures of job satisfaction, Avatar uses a three-point composite measure to capture employee engagement, which includes job satisfaction, inspiration to do more than accomplish minimum requirements, and likelihood of proudly recommending the organization to others. Engaged employees not only enjoy their work, but are willing to go the extra mile, and inclined to bring others along for the ride. Avatar s theoretical approach is that the employee engagement composite itself is a measure of individual qualities, while the key drivers of engagement are organizational qualities. These organizational qualities reflect the strategies and tactics an employer uses to interact with and facilitate development of their employees. The drivers of engagement facilitate the environment in which employees become engaged. Employers should consider measures of engagement as an outcome measure. They should rely on employee perceptions of organizational qualities to determine where to focus efforts for the purpose of engaging their workforce. Benefits of Engagement Many clients in the healthcare industry are curious about the benefits of having an engaged workforce, especially in the era of CAHPS and monetary reimbursements based on patient perceptions of care. This section will briefly highlight the importance of facilitating an engaged workforce in the healthcare setting. Generally speaking, increasing overall levels of patient satisfaction is a major goal for healthcare agencies. Several external research endeavors have demonstrated a link between measures and correlates of employee engagement and reported patient satisfaction. Job satisfaction and organizational loyalty have been linked to patient satisfaction with care and consumer loyalty, indicating that both patients and care providers generally agree on the quality of interactions during patient experiences (Clark et al., 2006). Similarly, scientific literature indicates that job satisfaction, an aspect of engagement, impacts nurse retention, which in turn increases quality of care and levels of patient satisfaction (Gross, 2003; Newman et al., 2001). Employee job satisfaction has also been linked to higher levels of patient
satisfaction with employee behavior, promptness, and expertise (Ott & van Dijk, 2005), as well as overall measures of patient satisfaction with care (Rondeau & Wagar, 2006). In addition to these general measures of patient satisfaction, key results are often impacted by higher levels of employee engagement. As mentioned, employee loyalty, a key component of engagement, is related to patient loyalty (Clark et al., 2006). Atkins, Marshall, and Javalgi (1996) also found a strong association between nurse job satisfaction and the likelihood the patient will recommend the hospital where they experienced care. A similar study found the inverse of that finding, indicating that likelihood of recommending a hospital decreased by two percent for every 10% of nurses that expressed dissatisfaction with their jobs (McHugh et al., 2011). Even aside from patient measures of satisfaction with care, research has indicated that higher levels of employee engagement in healthcare settings garner additional benefits. Engaged employees are found to have lower turnover rates, which facilitates a more experienced employee population, making processes run smoother, mistakes less frequent, and staff shortages less problematic (Gross, 2003). Higher levels of job satisfaction are also associated with reduced stress, fewer leaves of absence, and lower levels of work-related disability and workplace violence claims (Joiner & Bartram, 2004; Harmon et al., 2003). Engagement Drivers As indicated, employee engagement has an instrumental effect on the outcomes of an organization. But where does one start when attempting to improve engagement? This section will explore the key engagement drivers, or organizational qualities, that impact engagement. Generally speaking, most inquiries into what drives engagement are focused on the survey item level. However, this approach suffers from tunnel vision and is a manifestation of the desire for one solution that will solve all problems. In reality, a more holistic view of an organization is required, given the number of interrelated concepts and measures at play. Thus, the discussion of drivers here begins with the nine composites identified as drivers of employee engagement. Figure 1 displays the key drivers of engagement at the composite level. The composites were identified using Principal Components Analysis, which is an analytical technique that identifies patterns within the data to highlight related groups of items, rather than just relying on what items appear to be related to each other. The relationships between each composite and its effect on employee engagement are displayed in terms of the percentage of shared variance, or the degree to which variation in the employee engagement composite can be explained by the composite in question.
For example, explains 70.3% of the variance found in employee engagement 1. All relationships are statistically significant (p <.05). Figure 1. 80% 70% 60% 70.3% Key Drivers of Employee Engagement 65.9% 64.7% 50% 53.1% 40% 30% 45.2% 38.6% 35.2% 32.9% 20% 24.5% 10% 0% As shown, there is a general top-down pattern to fostering employee engagement. The strongest drivers lean towards higher-level concepts of, Work Atmosphere, and Senior Management. From there, the application and alignment of these concepts manifest in Skill Fit & Development, Orientation & Training, and Supervisors the last of which reflect the personnel that essentially act as a conduit between individual employees and the higher-level organizational functions. Finally, there is a cluster of more individualized elements and concepts that manifest on a level more easily identified by the average employee Accountability, Coworkers/Teamwork, and Individual Workload. 1 For the mathematically inclined, there is a reason why the sum of Key Drivers explains more than 100% of the variance within employee engagement. Each of the composites is interrelated in their own right there are strong associations between and Senior Management, for example. Measures of shared variance are based on bivariate relationships, so the overlapping interrelationships between these predictor composites are ignored for the sake of brevity.
At this point, one of the most frequent questions asked is why don t we just focus on the top 3 Key Drivers? This is another manifestation of the previously mentioned desire for a silver bullet. There are two major reasons why this is not a good way of thinking about potential problems. The first is that it ultimately promotes tunnel vision. The second is that it ignores other parts of the equation in terms of designing improvement efforts. Regarding tunnel vision and silver bullets, it needs to be recognized that each of these composites represent one part of a larger whole, and a holistic mindset is key in fostering an engaged workforce. All parts must be considered when developing improvement strategies, as all aspects of engagement are interrelated. Instead of focusing on one item within the composite, look at what intervention technique could target multiple items within that composite. Additionally, consider how your efforts targeting could be tied to improvement in other composites, such as Senior Management. Furthermore, although some composites contribute less to fostering engaged employees, it should not be interpreted that these composites are not important. Many of these composites include must haves that reflect bare minimum requirements. Good performance in these items may not drive engagement, but not accounting for them will hinder engagement efforts. To use an analogy, the key driver of an automobile is clearly the engine. However, completely ignoring the tires will prevent one from getting very far, and disregarding the climate control will not make for a pleasant trip. Restricting oneself to tunnel vision will cause information blind spots, which can ultimately cripple attempts at improving engagement levels in the workplace. The second reason to go beyond Key Drivers is because focusing solely on Key Drivers does not take into account other contextual elements notably, comparative performance (i.e., percentile rank). Knowing how one ranks within a comparative database helps provide an understanding of how hard it is to perform well on a particular measure. Occasionally, appearances can be deceiving. For example, one client may score 78% favorable for a particular measure of interest and devote extensive resources into improving that measure, but in reality they may be performing in the top 5% despite what they may view as a low score. Meanwhile, that same client is ignoring another item of seemingly less interest where their performance is abysmal, simply because it is on the lower end of the spectrum of Key Drivers. It is often more efficient and a better holistic approach to invest in improving the items where performance is worse. After all, it would be difficult to foster an engaged workforce if only 22% of employees are responding favorably to measures about Individual Workloads. In sum, comparative performance provides the context of how well an organization is performing on any particular measure, and helps each organization prioritize their specific improvement efforts.
Item-Level Drivers As beneficial as it is to focus first on composite-level drivers of engagement and performance, occasionally it is useful to dive a little deeper to understand item-level drivers of employee engagement. Table 1 presents these item-level drivers from most to least potent drivers, based on the full recommended Avatar 20/20 Survey Item Bank. Table 1. Item-Level Driver Coefficients for Avatar 20/20 items. Item Text Dimension Driver Coefficient There is a culture of trust in this organization. 0.653 I can explain this organization's mission to others. 0.630 Employees here have a good knowledge of this organization's corporate values. 0.622 Senior management responds to my problems in a fair manner. Senior Management 0.590 Senior managers visit my department frequently enough. Senior Management 0.587 I feel free to discuss work concerns with leadership above my immediate supervisor. Senior Management 0.577 My supervisor is friendly and helpful. Supervisor 0.555 My job gives me an opportunity to learn new skills. Skill Fit & Development 0.549 The guidance I receive from my supervisor is helpful to me. Supervisor 0.544 My job responsibilities contribute to my professional development. Skill Fit & Development 0.536 Employee innovation is encouraged at this organization. 0.533 My supervisor makes people in our work group feel that they are a valued members of the team. Supervisor 0.528 My job gives me an opportunity to do the things that I do the best. Skill Fit & Development 0.519 The workload in my work group is fairly distributed. Work Atmosphere 0.507 My immediate supervisor's actions are consistent with what he/she communicates. Supervisor 0.507 My supervisor has enough job knowledge to make decisions about my work. Supervisor 0.492 I understand how my job contributes to the mission of this organization. 0.465 This survey will result in change for this organization. Senior Management 0.454 Senior management of this organization is concerned about the employees. Senior Management 0.427 Employees at this organization are held personally accountable for the results of their work. Accountability 0.403 The people in other departments on whom I have to depend are doing a good job. Accountability 0.402 There is good cooperation between my department and other departments. Coworkers/Teamwork 0.395
Item Text Dimension Driver Coefficient The orientation for new employees is effective. Orientation & Training 0.368 The amount of work I am asked to do is acceptable. Individual Workload 0.362 This organization supports balancing work and personal life. Work Atmosphere 0.352 The organization provides effective on-the-job training. Orientation & Training 0.345 Organization policies are clearly communicated. Orientation & Training 0.292 Enough people are available in my work group to accomplish the necessary workload. Individual Workload 0.283 The necessary materials and equipment are available when I need to perform my job. Work Atmosphere 0.249 I have enough authority to accomplish the work that is expected of me. Accountability 0.231 There is good cooperation among the members of my work group. Coworkers/Teamwork 0.200 The order of item-level drivers in the table is sorted by a calculated variable called a Driver Coefficient. The Avatar 20/20 composites were determined using Principal Components Analysis. This type of analysis essentially buckets individual items into groups and calculates a Factor Loading, an indicator of how strongly related each item is with their assigned group. The Driver Coefficient takes this Factor Loading and multiplies it by the parent composite s correlation coefficient with the employee engagement composite. Thus, some surrounding contextual information is preserved as the calculation takes into account the degree to which the item represents its parent composite, as well as the relationship between that composite and the employee engagement composite measure. Since we are dealing with composite measures, we are better able to account for measurement error, which allows us to identify a more precise relationship between composites. Taken together, the Driver Coefficient essentially leaves us with an abstract measure of item-level importance to employee engagement. Survey items with larger numbers have more of an impact than items with smaller numbers. However, it is important to note that this is only in terms of general trends individual organizations must also look at comparative performance as part of the equation. In sum, this approach is more effective than simply examining correlation coefficients between items and engagement, as that is a simple bivariate analysis that ignores the complexities accounted for here. Conclusion To effectively improve engagement at an organization, it s important to understand the interaction between the organization and the individual employee. By viewing employee engagement as an outcome measure, organizations can identify what drives it. Understanding these drivers will allow for the development of the most impactful improvement plans.
References Atkins, P.M., Marshall, B.S., & Javalgi, R.G. (1996). Happy employees lead to loyal patients. Journal of Health Care Marketing, 16(4), 14-23. Clark, P.A., Wolosin, R.J., & Gavaran, G. (2006). Customer convergence: Patients, physicians, and employees share in the experience and evaluation of healthcare quality. Health Mark Quarterly, 23(3), 79-99. Gross, J.W. (2003). Happy employees, happier patients. Healthcare Executive, 18(5), 68-69. Harmon, J., Scotti, D.J., Behson, S., Farias, G., Petzel, R., & Neuman, J.H. (2003). Effects of high-involvement work systems on employee satisfaction and service costs in veterans healthcare. Journal of Healthcare Management, 48(6), 393-406. Joiner, T.A., & Bartram, T. (2004). How empowerment and social support affect Australian nurses work stressors. Australian Health Review, 28(1), 56-64. McHugh, M.D., Kutney-Lee, A., Cimiotti, J.P., Sloane, D.M., & Aiken, L.H. (2011). Nurses widespread job dissatisfaction, burnout, and frustration with health benefits signal problems for patient care. Health Affairs, 30(2), 202-210. Newman, K., Maylor, U., & Chansarkar, B. (2001). The nurse retention, quality of care, and patient satisfaction chain. International Journal of Health, 14(2-3), 57-68. Ott, M., & van Dijk, H. (2005). Effects of HRM on client satisfaction in nursing and care for the elderly. Employee Relations, 27(4/5), 413-424. Rondeau, K.V., & Wagar, T.H. (2006). Nurse and resident satisfaction in magnet long-term care organizations: Do high involvement approaches matter? Journal of Nursing Management, 14(3), 244-250.