Support service environments are stressful with stringent demands on individual and workgroup performance that have to be met day aer day. In earlier work we have modeled the impact of stress within such environments on the performance of the individual and correspondingly that of the team. Since teams are social environments, we can intuitively realise that social dimensions such as supervisory support would impact a team member's performance for the beer or the worse. But what is the precise impact of supervisory support on a team's macro outcome parameters such as productivity and performance? Using the results of a ground study of a support services organization, we use an agent based simulation approach to understand the dynamics and the implications of supervisory support on individuals and consequently the macro parameters of the team. We show that supervisory support plays a critical role in ensuring that the team as a whole meets its performance parameters particularly in the presence of disruptive factors such as work spikes.
•Computing methodologies ! Agent / discrete models;
Employees in support services organizations work as a part of large
teams. ese teams are expected to reach very competitive targets
from their business clients in industries such as nance, retail,
banking, health-care etc. e targets are specied in service level
agreements (SLAs) which indicate aspects such as the Mean Time to
Resolution (MTR), Turn Around Time (TAT) for dierent categories
of tasks as well as the escalation hierarchy in case of emergencies.
e organizational environment in which these associates work is
stressful and requires individuals within the team to rely on each
other as well as their supervisors and leadership in order that the
tasks are done as specied in the SLA. Studies in such environments
[
In this work, we extend these models to examine how organizational social dimensions impact workplace outcomes. In particular we study how the organizational social dimension called supervisory support may impact workplace outcomes in case of a support services organization. Past studies including our own show that supervisory support impacts team member characteristics such as engagement, job satisfaction, absenteeism and productivity. In this work we use an agent based system to study the dynamic implications of supervisory support on macro parameters of a prototypical support services team. 2
Past research on the role of supervisory support has highlighted its
benecial impact on a range of individual, team and organizational
outcomes. Supervisory support is described as the employees’
perception of the extent to which supervisors value their contributions
and care about their wellbeing [
Before we go on to discuss the context, we introduce a few terms
that will be used in rest of the paper. Below we dene some of
the study variables that have been referred to in the following
discussion: Emotional state refers to an individuals experience of
positive and negative emotion with respect to their work, at a
specic point of time during the work day, namely at the start of their
work day and at the end of their work day. Momentary stress
refers to the perception of stress related to work at the start and end
of the individuals work day. Workload refers to the number of
tasks arriving on a day, to be completed by an individual before end
(a) Without supervisory support
(b) With supervisory support
of the day. Aect is the extent to which the associate experiences
positive or negative mood during the course of the work day. In
this study, we are focusing only on the negative aect. Workload
spike refers to a 1.75 times increase in workload on a particular
day (exceptional day). Backlog refers to the number of pending
tasks for an individual at an instance of time. Bench strength
refers to individuals in the workforce that are used only during
crisis situations like: heavy workload arrival or large number of
unplanned absentees on a day, etc. Supervisory support refers to
perception of employees regarding the degree to which the
supervisors value their contributions and care about their well-being [
A large support services organization had been facing issues
with its employees of unscheduled leave or absenteeism as well as
Description Source
Perception of workload has a positive impact [
High stress (> 0.9) was correlated with high Field Study absenteeism decreases in team productivity. e organizational structure had one supervisor leading a team of several hundred associates. e supervisor was responsible not only for ensuring that SLAs were met on daily basis, but also were required to frequently monitor individual learning and performance particularly for newcomers to the team. It was also the supervisors role to maintain team morale on days when there was a heavy spike or accumulated workload due to absentees among the team, seasonality or other factors.
We had carried out an exploratory study in the account teams
identied by the support services organization to examine the
impact of static (trait) as well as dynamic (state) behavioral factors
on the outcomes of interest, i.e. absenteeism and productivity.
Elements of our study ndings pertaining to individual traits and states
such as conscientiousness, aect and stress, have been reported
in [
In the eld study, we also observed that, associates who perceived lower supervisory and coworker support reported lower engagement and job satisfaction (p < 0:05). Similarly, higher perceived supervisor support was linked to higher objective ratings, productivity and quality as well as perceived engagement and satisfaction (p < 0:05). In parallel, higher coworker support was linked to objective ratings and perceived engagement and job satisfaction.
In addition to the above analysis, multiple regression also showed the signicant impact of supervisory support on job satisfaction (b = 0:27; p < 0:05) and stress (b=-0.27, p < 0:01). e combined eect of stress and supervisory support on productivity was also signicant (b = 0:21; p < 0:01) indicating an indirect eect of supervisory support on productivity in the presence of stress. In other words, the buering eects of supervisory support described in past research were also supported by the empirical ndings in our eld study. is nding also lends support to our model presented in section 3.1 where we include supervisory support as a moderator in the stress!productivity relationship. In our review of the research on supervisory support we have yet to nd a study that models the dynamic eects of supervisory support on productivity. is therefore is a key contribution of the present study.
us, support from the larger organization, particularly the supervisor emerged as one of the important insights from this study as we found that supervisory support was linked to both objective performance outcomes measured by the HR team as well as perceived outcomes measured in our survey as discussed above. is result from the study was further supported by in depth interviews with the associates, supervisors and senior leadership in both the teams that participated in the study. ese demonstrated the close ties that the supervisor had with the rest of the team despite the large spans of control.
Given the importance of supervisor support in terms of providing consistent role modeling, mentorship, counseling and guidance to their reportees, the present study examines the following research questions: ”How does supervisory support at an individual level aect dynamics at the team level, in its presence and absence?” e next section presents the model, experiments, and results obtained. 3
ese insights on dimensions of behavior and potential for impact
on outcomes, led naturally to the need to understand the dynamic
implications of these ndings. We have been using a grounded ne
grained agent based simulation approach to explore these
implications and which have been reported in [
For conducting the simulated experiments for our process model,
we have chosen the GIS and Agent-based Modelling Architecture
(GAMA) [
e tasks are assigned on daily basis with a mean of 1000 tasks per day and std. deviation of 10%. We monitor the running simulation for 1200 cycles which are equivalent of 120 simulation-days. Average tasks received per day are 1000, with variation of 10%. A spike in workload implies 2000 tasks.
Each simulation of is executed 10 times for every combination and the mean is reported as the nal parameter value. During these runs, we collected data for variables such as average turn-around time, average backlog, and average stress. ese data are visualized in following charts. 3.3
In this section, we discuss impact of supervisory support on average TAT, backlog, and stress in presence and absence of workload spike. In addition, these scenarios are simulated against bench strengths of 0% and 6%.
Fig. 3 informs us of the importance of supervisory support. e chart shows us eect of spike on average TAT for dierent bench strengths, in presence and absence of supervisory support.
First, we will discuss case without supervisory support. If the team has the 0% bench strength, then without spike average TAT was 16 days, which jumps to 65 days in presence of spike. With bench strength of 6%, the average TAT falls from 16 days to 8 days without spike, and from 65 days to 55 days in handling a spike, which is a small 10% reduction.
With supervisory support, the team with 0% bench strength can turn a task around in 5 days in absence of spike, which is approximately a third of earlier 16 days, and same for bench strength of 6%, which is 33% reduction. However, interestingly, the team also mitigates spike in the workload in the same envelope of 5 days, a reduction from 65 days and from 55 days respectively.
us, we see that teams with high supervisory support can deal even with work spikes without signicant impact on TAT, while a team that lacks supervisory support shows both higher average TAT without a spike as well as a signicant jump when there is a spike. is is macro-level eect, and it can be aributed to a slower rise in the stress at an individual level, when supervisory support is available.
Similarly, we see in Fig. 4 that the average team backlog increases
without supervisory support in absence of workload spike, and
increases substantially further in presence of such a spike. Please
note that this chart uses the log axis for representing average
backlog, to accommodate large dierences. e primary reason why
supervisor support has such a dramatic eect on TAT and backlog is
because supervisory support reduces stress levels of team members.
Past research also supports this result wherein supervisory support
is linked to lower levels of stress among employees by acting as a
buer against work related stress [
Fig. 5 shows average cumulative stress levels of individual team members (accumulated per member over simulation duration)with and without supervisory support and with dierent bench strength. With supervisory support, the stress goes down visibly from approx. 850 to 700. In the presence of moderating interaction of supervisory support, the stress grows much slowly than in absence of supervisory support. is slower growth has a substantive impact on various outcome parameters. We also see that increase in the bench strength from 0% to 6% to counter poor supervisory support does not reduce average stress in the team members. 4
In this paper, we have explored the implications of supervisory support on work outcomes using an agent based model. We show that supervisory support has signicant impact on TAT and work backlog. We conjecture using our model that this impact is because supervisory support helps mitigate stress caused by negative aect.
is work extended our earlier work on how individual traits and states impact work outcomes by considering the organizational social dimension of supervisory support. We plan to further extend this by studying peer to peer impacts as well as group level eects.