The contextual role of subjective age in the chronological age/absenteeism relationship in blue and white collar teams

ABSTRACT

Organizations in the twenty-first century face the challenges of an increasingly ageing workforce, which have an effect on organizational health and productivity. As chronological age has shown to be an insufficient indicator of employees’ health and, in particular, absenteeism, we apply the subjective age concept (i.e., how old an employee feels) at the team-level to explain the average chronological age/average short-term absenteeism relationship. We develop a theoretical framework for underlying processes, combining the subjective age research with the socioemotional selectivity theory and team contagion processes. We test our predictions in a time-lagged team-level sample of 1,015 teams with 12,926 employees to find a significant interactive effect of average chronological age and average subjective age on average short-term absenteeism in teams. The relationship is negative and significant when average subjective age is low. Under high-average subjective age, the relationship is non-significant. Furthermore, this interactive effect (average chronological age/average subjective age) is moderated by job type (white versus blue collar) in the form of a three-way interaction, indicating that the effect is only significant among white collar teams. We hope to enrich the theoretical debate on age and absenteeism and provide organizations with a new perspective on ageing work teams.

KEYWORDS:

• Subjective age
• short-term absenteeism
• white collar
• blue collar
• demographic change

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

1. For some variables, more information was available. For most of the control variables 1,018 teams were available, while we had data from 1,045 teams for the outcome variable.

2. We tested a model controlling only for average short-term absenteeism T₁ and a model controlling for both short-term T₁ and long-term absenteeism T₁ separately. Results show that the three-way interaction stays significant in both cases. This strengthen our results and justify our control variables choice.

3. For internal anonymity reasons of the company, teams with less than seven employees or less than seven survey results were excluded from all team data sets.

4. For most of the control variables, 958 teams were available, while we had data from 980 teams for the outcome variable.

5. To ensure that we correctly allocate blue collar leaders to the blue collar job type, we gathered information about the job characteristics of blue collar leaders. Based on interviews with HR managers, we know that blue collar leaders do the same job as their subordinates with the additional task to organize team meetings and work plans as well as manage internal communications. Thus, we justify the classification of blue collar leaders to the blue collar job type.

6. All results of these robustness-tests are available upon request from the first author.

7. To reach the VIF values, we replicated our results with OLS regression techniques leading to similar regression coefficients as our main analyses, thus increasing the confidence in the robustness of our results.