Tailoring interventions for those in most need: Grouping employees by

burnout and engagementCarolyn Timms and Paula Brough

Griffith University

Rationale• Most engagement and burnout research is based on

aggregated data and uses statistical techniques that are out of reach of ordinary practitioners and management personnel.

• An important limitation of our continued dependence on quantitative research could be that individual perspectives may be lost or overlooked in broad sweeping strokes provided by statistical analysis.

Aims• To group respondents according to their specific burnout and

engagement responses

• To identify the resulting group similarities of workload, job control, reward, community, fairness and values (Areas of Work-life Survey, AWS).

• Overall aim: to illustrate the work perceptions of the most at-risk workers, with a view to tailoring interventions specifically for the employees in most need.

Method

• Two independent samples of employed workers in Australia who responded to a survey.

• Sample one (n = 953) were employees of an education union

• Sample two (n = 260) was a mixed group of employees representing a number of organizations.

DemographicsSample one

• N= 953

• All members of an education union

• 701 (74%) female & 252 (26%) male

• Mean age 45 to 49 year age banding

• Mean hours worked =46 hpw (SD = 12.88)

Sample two

• N=260

• Various professional groups

• 155 (60%) female & 105 (40%) male

• Mean age 40-44 year age banding

• Mean hours worked 45 hpw (SD = 12.55)

K-means cluster analysis

• Previously used in medical research to identify individuals in most need of tailored interventions

• Used in molecular biology for gene expression data analysis

• Current research used a predetermined number of five clusters to represent five points in the continuum between the most engaged and the most burned out workers.

Used to define clusters• The Oldenburg burnout Inventory (OLBI)

• Exhaustion & Disengagement

• The Utrecht work engagement scale (UWES)– Absorption, Vigour & Dedication

The research sought to determine people who were most alike on these variables to group them into clusters.

• Standardised – z scores

18%

26%23%

24%

9%

Differences between clusters and the AWS

• According to Clatworthy et al. (2005) it is important to use some variables that have not been used in defining the clusters in order to demonstrate that the clustering is valid.

• All AWS variables demonstrated significance between clusters (MANOVA analysis).

MANOVA - AWSF η2

Control 34.56*** .13

Workload 58.93*** .22

Reward 39.14*** .15

Community 27.19*** .11

Fairness 36.83*** .14

Values 50.83*** .18

X

X workload

X

X

XX

AWS

Findings• Five groups that differed from each other on

burnout and engagement were found.

• These groups demonstrated different patterns of response in regard to the AWS, which provided an indication of their workplace experience.

Findings

• People can experience aspects of engagement (e.g. absorption) and burnout (e.g. exhaustion) simultaneously. Further investigation may find some interesting interactions between these variables.

• The research has demonstrated how the needs of workers differ and suggests that this method of analysis may provide some suggestions as to how more effective interventions may be tailored.

Examples of possible interventions using Cluster Analysis

• Identification of a group in the workplace such as the ‘under-pressure group’ (who reported moderate engagement scores as well as exhaustion) suggests that interventions addressing workload and work-life balance issues may be more effective strategies for improving the intrinsic motivation of these workers.

Examples of possible interventions using Cluster Analysis #2

• Identification of large numbers of people whose responses are similar to the un-engaged group may suggest job enrichment schemes might be effective strategies.

• Identification of people who are burned out within an organisation suggests focused management education and training as well as improved organisational communication.

Conclusion

The five groups identified in the current study have differing needs.

This demonstrates that ‘one size fits all’ approaches to organisational interventions is not necessarily the most effective approach.

Approaches that target these groups are therefore strongly recommended in the interest of improving individual and organisational outcomes.