System Dynamics Approach for Managing Turnover Problem in
Professional Service Firm
Arry Rahmawan Destyanto, Armand Omar Moeis, Akhmad Hidayatno, M Rizky Nur
Systems Engineering, Modeling, and Simulation Laboratory
Department of Industrial Engineering, Universitas Indonesia
Kampus UI Baru, Depok, 16424
Email : firstname.lastname@example.org, email@example.com, firstname.lastname@example.org,
Talented human resources is one of the key factors in a firm’s success to maintain its
competitive advantage in the global era. The rising trend of employee turnover is causing
firms to rethink their strategy in maintaining their talented human resource. This issue
challenges firms especially that rely on talented human resource such as professional service
firms. Turnover has direct and indirect negative impact for firms. Previous researches had
been conducted research to understand the underlying problem that causes turnover, and the
strategy to overcome it. This research calls for a modeling structure of the turnover
phenomena using a system dynamics approach, which can also be used to simulate various
strategy. The output of the model is a recommendation of strategy that is most effective for
reducing turnover and has the best return on investment for organization. The results of
simulation study will be discussed at the end of the paper.
Voluntary Turnover, System dynamics, Human Resource Management, Causal Loop
Diagram, Feedback Loop, Simulation
Turnover is one of the main focus of research in the field of human resources
management, as it has a significant negative impact on business and organization
performance (Tariq, Ramzan, & Riaz, 2013). One of the main industrial sector that is highly
vulnerable to the turnover problem is the professional service industries. The rise of turnover
phenomena from year to year in that sector has caught many interest on research of the topic.
One of the direct impact for the firms is the degrading of their competitiveness resulted from
lower productivity and cost overrun (Cascio, 2006). Besides that, organization will have
vacancies in the middle management for few years, which is a challenging issues for firms
who rely on the competencies and capabilities of their talented human resource.
Research on turnover recently can become very complex as there are many factors
and relationship among variables that are affecting turnover. To tackle the issue, a holistic
approach is needed to view the problem as whole. This research aims to recommend a
strategy that has the highest effectiveness using system dynamics approach. By using this
approach, we aim to handle the problem with higher effectivity as the affecting factors are
modeled in a structured manner, so that the problem owner can explore various strategies to
tackle the issue.
Research on turnover in the last two decades mostly discussed about the correlated
relationship between the affecting variables in both statistically quantitative (Nouri & Parker,
2013; Aluntas, 2013; Chen, 2013) and correlated qualitative (Rowbottom, 1977; Krogh,
1995) manner. Research using a predictive model was also developed using multivariate
analysis (Kane – Sellers, 2007). A dynamic model in the field of human resource
management was also developed to study the issue on burnout in work (Skok; Zoroja; Bach,
2013), and in other cases on knowledge management (Aburawi & Hafeez, 2009). In this
research, the analysis of turnover is carried out using a dynamic structure to represent the
better structure of the problem.
The contribution proposed from this research is to apply system dynamics simulation
approach to evaluate the effectiveness of human recource management intervention strategy
in handling turnover problem. By using system dynamics, the turnover problem is expected
to be handled more effectively due to the structured manner of the model.
Moreover, besides identifying an effective strategy, firms also have the concern on
the financial benefits. Every strategy that is implemented to handle turnover, has a relatively
high financial compensation. This criteria causes a dilemmatic problem between return on
investment and the effectiveness of the strategy. Therefore, the model includes the financial
criteria to see which of the strategy is feasible for implementation in the organizations.
HRM Intervention on Turnover
Turnover can be define as the movement of employees from one organization to
another in which they no longer have obligation to the previous organization (Allen, 2008;
Choi, Musibau, Khalil & Ebi, 2012). Turnover can be initiated by the employees themselves
(voluntary) or also by other factors (involuntary), for example initiated by the organization
or other intangible factor such as death, illness or retirement (Perez, 2008). The term
employees’ turnover in researches refers to ‘voluntary turnover’ (CIPD, 2011). Voluntary
turnover is the focus of research in which it gives a negative impact on firms, this issues is
known as ‘dysfunctional turnover’ (Morrell, 2011). The impact resulting from dysfunctional
turnover is the decrement of work productivity (Batt, 2002), over budgeting to replace the
leaving employees (Cascio, 2006), and the loss of regeneration for the firm (Collison, 2005;
The turnover phenomena can be explained through several theories concerning on
organizational behavior. ‘Social exchange theory’ states that every human interactions is in
the goal of maximizing benefits (Brinkmann & Stapf, 2005). Adams (1963) explained that a
person have a high motivation of work when is given a fair amount of compensation in
compare to the workload. Besides, an equally fair treatment between colleagues is one of the
factors that influences employees’ loyalty. Stigler (1961) brought up the idea of one’s
consideration for other opportunities when there are unfulfilled expectations. When the
opportunity comes in, they will seize it by moving to the other organization.
Allen (2008) uses the term ‘drivers’ to describe the factors leading to turnover.
Psychological condition in the working environment such as the level of work pressure, high
workload, and the work-life balance can be the drivers for employees’ intention to leave
(O’Neil & Xiao, 2010; Shani & Pizam, 2009). While other psychological factors such as job
satisfaction, organizational commitment, and cultural fit are the factors that can minimize the
intention of employees to leave (Kane-Sellers, 2007; Powell & Mayer, 2004). Other working
environment factors such as salary, compensation, and facilities, training and development,
recruitment process, and work involvement can also be the drivers for turnovers (Kane-
Sellers, 2007; Allen & Griffeth, 2001). Demographical factors such as age, education,
gender, and ethnical background can also become drivers for turnovers if they are considered
minority in the workplace (Jones & Harter, 2005; Kirschenbaum & Weisberg, 2001).
Based on previous research, it was discovered that turnover can be managed by
implementing the right strategy. Few interventions that has been proven successful to manage
turnover are through improving the recruitment strategy (Breaugh & Starke, 2000),
improving the candidate selection (Griffeth & Hom, 2001; Hunter & Hunter, 1984),
providing various self-development opportunities through trainings (Hom & Griffetfh,
1995), and also by providing various incentives such as bonus, rewards and competitive
compensation (Heneman & Judge, 2006).
Interventions through human resource management puts its focus on two most
indicators for organization, first is turnover rate in a period that is deemed acceptable and
‘high turnover’ as the measurement of work productivity. Another indicator is to see the
turnover cost that can be saved by implementing the strategy, whether it is tolerable or
intolerable. These two indicators are the basis for evaluating the best strategy to manage
turnover (SHRM, 2010).
Prior to the development of the system dynamics model, we obtained data from a
professional service firm in Jakarta from the year 2013 – 2014. The collected data were
processed to identify the current turnover trend the firm as the basis of hypothesis of the
model. Based on expert review, historical data, and forecasting, the trend of turnover of the
firm is shown in figure 1.
Figure 1. Turnover trend in professional service firm as the basis of reference mode
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Figure 1 shows the graphical trend of turnover from the year 2009 through 2020.
Historical data were used up to the year 2014 and as for 2014 onwards, a forecasting method
is used through an assumption of no intervention is made to the existing conditions. The trend
is used as the reference mode for the simulation model.
TURNOVER SYSTEM DYNAMICS MODEL FORMULATION
The development of the model was started by constructing the mental model concept
of how turnover can take place. In the process of the mental model concept development,
literature review on previous researches is done intensively. For the written and numerical
data were obtained from the professional service firm in Jakarta as a case study for
developing the structure of turnover problem. Data collection was conducted through
unstructured review or also known as ‘intensive interview’, in which the selected respondents
were the most experienced in that firm (Luna-Reyes, 2005). The output of this phase is the
causal-loop diagram as the visualization of the interactions amongst the affecting variable as
shown in figure 2.
Figure 2. Model conceptualization of turnover problem in professional service firm
The causal-loop diagram explains how turnover and the financial performance work.
Technically, turnover is the percentage of the number of employees that leaves the
organization in each year in compare to the whole number of employees. On the other hand,
return on investment is calculated from the number of benefit received by the firm (in terms
Performance Project Gr owth
Desired Number of
High Sala ry
Backlog of task
Development Support Pay Satisfaction
Desirabil ity of
B2 Pay Satisfaction
of turnover cost saving and other financial performance improvement) subtracted by the
strategy cost, and in ratio to the total cost. Subsequently, the impact is averaged per year.
Based on the data collected through ‘exit survey’ of 1622 active employees and 1785
ex-employees, several factors that drives them to stay in the organization as well as their
impact as their impact on retention are shown in table 1.
Table 1. Factors affecting employees’ intention to stay in an organization
Pay Equity / Pay Satisfaction
Historical Compensation Growth
Anticipated Compensation Growth in 5 Years If Stay at Firm
Anticipated Compensation Growth in 5 years If Leave the Firm
Perceived Job Alternative
General Job Factors
Professional Skills and Development Related Factors
Fit with Professional Services
+3 Big Positive Impact
+2 Positive Impact
+1 Small Positive Impact
0 No Impact
-1 Small Negative Impact
-2 Negative Impact
-3 Big Negative Impact
Following to the identification of the factors, a logistic regression equation was
formulated to predict the voluntary turnover that will generate the output of the model.
Logistic regression was chosen due to the underlying linear regression with dependent
variable that have a stochastic and binary (turnover or leave) characteristics. The logistic
regression equation is written as follows,
To determine the number of employees leaving, a mathematical model is included in
the stock and flow model, which has been adjusted as follows,
For every value represents the coefficient that generates the log of the probability
ratio for the variable, that can be used to determine the amount of impact each variable has
on the probability of turnover. For example, 2 can be interpreted as a quantitative form of
each increasing value of ‘pay satisfaction’ and its impact on the probability of employees
leaving (increase or decrease).
The total annual cost of turnover is used as the basis of the return on investment
calculation. On the other hand, benefits that are directly related to the implemented strategy
will take away the total annual cost of turnover, which subsequently compared to the amount
of investment made on the implemented strategy. The equation of ‘total annual cost of
turnover’ is formulated as follows:
Total Annual Cost of Turnover
= Total Cost of Turnover (per employee) x Number of Leaving Employees (per year)
Quantitative data used in the model was adopted based on data collected from the
annual report of firms from the year 2006 to 2013. While to formulate the relationship of the
variables which has qualitative characteristics and included in the soft variable category, the
data collection was conducted through mental mapping from experts using Likert scale.
The output indicator of the model is the percentage of employees leaving in one year.
The other output indicator is return on investment of each implemented strategies. A stock
and flow model is constructed based on a conceptual model as shown in figure 3.
Figure 3. Stock and flow model of employees’ turnover
MODEL TESTING, VALIDATION, AND VERIFICATION
Constructing a system dynamics model in the field of human resource management
has its own challenges. Apart from the numerous variables involved in the soft variables
category (difficult to quantify), the structure that produce the behavior of turnover could also
become very different among organizations (doesn’t rely just on one structure).
Therefore, the model that was constructed to evaluate the strategy for managing
turnover was needed to be verified and validated. Several technique of verification and
validation was conducted so that the model could reach a high level of confidence, quite
enough to simulate several strategies of managing turnover and consequently produce the
Verification test was conducted by ensuring dimensional consistency of the model.
This is shown by the absence of technical error during the simulation. Expert judgment on
the stock and flow model was also done to ensure that the computer based model congruent
to the conceptual model. One of the validation test conducted was through the behavior
reproduction and compare it against the reference mode. Based on the test, a behavior over
time is obtained from the model and compared against the real behavior as shown in figure
Figure 6. Behavior reproduction generated by the model in compare to the behavior observed in the real
The other test was conducted through extreme-condition test, sensitivity analysis of
the key variables, and validation based on experts to strengthen the model confidence
TURNOVER STRATEGY TESTING
Models that have been through the process of verification and validation could be
used to conduct experiments. The model will be used to test 4 strategies as an intervention to
control the turnover. The four strategies are: Training & Development, Engagement,
Compensation / Financial Incentive, and Recruitment. The parameters set for fourth test of
strategies are divided into two, namely to junior staff and senior staff. A more detailed
parameter setup can be seen in Table 2.
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Table 2. Parameter setup for controlling turnover strategy testing
Strategy for New / Junior Staff
Annual Training per
--------------------18 Hrs / Yr--------------------
Annual Coaching per
---16 Hrs / Yr---
18 Hrs / Yr
----------US$12,600 / Yr----------
US$15,000 / Yr
Strategy for Experienced / Senior Staff
Annual Training per
--------------------21 Hrs / Yr--------------------
Annual Coaching per
---16 Hrs / Yr---
24 Hrs / Yr
------------16 Hrs / Yr------------
----------US$38,400 / Yr----------
US$40,800 / Yr
Models of dynamic systems that have been constructed then executed within the
period up to 2024 to present how the form of its behavior over time. The output of the model
is shown in Figure 7.
Figure 7. Effects of Strategy on the output of the model
As for the return on investment output, it is calculated for each strategy. Calculations
carried out by using the present value of each return on investment earned each year and then
averaged as much as the current year (2014 - 2024) and the results are attached in Table 3.
Table 3. Return on investment of each strategy alternatives
Return on Investment (Estimation)
Training & Development Strategy
Based on the results of the model, furthermore it is processed into a form of SMART
Score Card (de Haan & de Heer, 2012) to facilitate the decision maker in making decisions.
The result is inserted into a table containing criteria and decision strategies. Assuming that
both criteria have the same weight, the result then processed to produce a normalized value.
Calculation of the formula is as follows:
Normalised value of x = (actual value – worst value)/(range)
The result of normalized value is presented in table 4, then the total is being
calculated. The result of this total value calculation obtained recommendations on the
sequence of which strategy is best for controlling the turnover in the company.
Table 4. Total value calculation of each strategy alternatives
Average on Reducing
Turnover Rate per Year
(Value : 6%)
(Value : 1.2%)
(Value : 7.3%)
(Value : 3.4%)
Average ROI per Year
(Value : 74%)
(Value : 38%)
(Value : -132%)
(Value : -65%)
Based on the calculation, then to be able to control the turnover in the future, the
company that became the object of our study is highly recommended to adopt a strategy of
training and development since it has the highest value. While the company is not
recommended to adopt a recruitment strategy because it has the lowest value.
DISCUSSION AND CONCLUSION
In addition to get the answer to what is the best strategy that can be adopted by
companies to control the turnover in their organizations, this research also aimed to show the
contribution of systems dynamics in generating better solutions for controlling the turnover.
The previous studies have been a lot to discuss about what are the factors that cause
the turnover, as well as how to overcome those factors. Various kinds of research have been
conducted by academics, companies and consultants, trying to find out what is the best
solution that can be implemented to control turnover issue.
This study is intended to complement existing research, where the approach used is
system dynamics. Previous approaches mostly tended to be static. One of the strengths of the
static model is the accuracy in determining the exact value in solving a problem. On the other
hand, this strength also becomes a weakness because it often makes us to not consider the
interconnection among other factors which also has an important role as a benchmark for the
success of an organization.
System dynamics models which have been constructed to consider two main
organization criteria in controlling the turnover, namely the aspect of performance illustrated
by the turnover rate as well as financial aspects which measured from the return on
investment. Once the model is simulated, it can be seen, the most effective strategy for
controlling the turnover rate is the compensation strategy, a strategy that is focused on
providing incentives material to talents available in the company to prevent them from
shifting. By implementing this strategy, projected average turnover per year in the company
could be reduced by 7%. By looking only to this aspect, certainly the company will
immediately implement a strategy to restrained turnover rate.
However, implementation of controlling turnover strategies would have a direct
relationship with the amount of the overall budget. For companies, the financial aspect is also
an important consideration and it must be ensured that the strategy adopted also provide
financial benefits for the company. Apparently, after the model is run, it was found that the
compensation strategy has the worst return on investment. Therefore, further analysis of the
alternative strategies of the model output is needed.
System dynamics can facilitate the interconnection between the complexities of
variables that might be limited in static models. After doing an analysis, it was found that by
considering two criteria which have been mentioned, the best strategy that can be
implemented by the company is training & development strategy, considering the impact
effectivity on turnover rate and return on investment were indicated to be good. Based on
these results, we learned that by understanding the structure and interconnection between
variables deeper using a system dynamics, it could make us be more prudent when making
such important decisions.
On the other hand, we aware that the model we put together still has some drawbacks.
Output models generated from this research may only be suitable for companies that we have
studied. As for companies from other industrial sectors or those which have a different work
culture, would need to be made models that suit the conditions of company or organization.
This will also certainly have an impact on different output recommendations.
Another weakness of this model, that it still not yet considering the uncertainty
aspects in the event of sudden changes of assumptions in the future. Current assumptions are
made along with the data and the condition of the company in 2014. Perhaps in 2016 and
years afterward there will be changes in the assumptions that make this model would be reset
to provide more accurate results output.
Regardless of its shortcomings, we hope this research can provide inspiration to take
better advantage of system dynamics approach in finding solutions in the field of human
resource management. However, doing simulations in the virtual world would be much more
profitable rather than doing live experiments in the real world due to minimal risk. Another
benefit, decision makers can take a lot of insight so that they will be able to make far more
wisely decisions of which strategy should be applied to control the turnover.
Aburawi, I., & Hafeez, K. (2009). Managing Dynamics of Human Resiurce and Knowledge Management in
Organizations Through System Dynamics Modeling. International Journal of Sciences and Techniques
of Automatic Control & Computer Engineering Vol. 3, No. 2, 1108 - 1125.
Ang, K. B., Goh, C. T., & Koh, H. C. (1994). An employee turnover prediction model: A Study of Accountants in
Singapore. Asian Review of Accounting, 121 - 138.
Barlas, Y. (1996). Formal Aspects of Model Validity and Validation in System Dynamics. System Dynamics
Review, Vol.12 No.3, 183-210.
Barlas, Y. (1996). System Dynamics: Systemic Feedback Modeling for Policy Analysis. In S. D. Society,
Encyclopedia of Life Support Systems (pp. 1131 - 1175).
Barlas, Y., & Carpenter, S. (1990). Philosophical Roots of Model Validation: Two Paradigms. System Dynamics
Review Vol. 6, No. 2, 148 - 166.
Cavana, R. Y. (2010). Scenario Modelling for Managers: A System Dynamics Approach. 45th Annual Conference
of the ORSNZ. Wellington.
Coyle, G. (1999). Qualitative Modeling in System Dynamics - or What are the Wise Limits of Quantification?
17th International Conference of The System Dynamics Society. Wellington: System Dynamics Society.
Coyle, R. G. (1977). Management System Dynamics. Chichester: John Wiley & Sons.
Coyle, R. G. (1996). System Dynamics Modelling. London: Chapman & Hall.
De Haan, A, & De Heer, P. (2012). Solving Complex Problem. Eleven International Publishing
Doyle, J. K., Ford, D. N., Radzicki, M. J., & Trees, W. S. (n.d.). Mental Model of Dynamics System.
Featherston, C. R., & Doolan, M. (2009). Using System Dynamics to Inform Scenario Planning: A Case Study.
31st International Conference of the System Dynamics Society. System Dynamics Society.
Forrester, J. W. (1961). Industrial Dynamics. Cambridge, Massachusetts: MIT Press.
Forrester, J. W. (1973). Confidence in Model of Social Behavior with Emphasis on System Dynamics . System
Dynamics Group Working Paper D-1967.
Godet, M. (2000). How to be Rigorous with Scenario Planning. Foresight: the Journal of Future Studies,
Strategic Thinking, and Policy, Vol. 40, No. 1, 5 - 9.
Godet, M. (2000). The Art of Scenarios and Strategic Planning: Tools and Pitfalls. Technological Forecasting
and Social Change 65, 3 - 22.
Grasi, O. (2009). Key Performance Indicators in Professional Service Firms. Wiesbaden: Transentis
Management Consulting GmBH & Co. KG.
Hafeez, K., & Abdelmeguid, K. (2003). Dynamics of Human Resource and Knowledge Management. Journal of
the Operational Research Society, 153 - 164.
Holmstrom, P., & Elf, M. (2004). Staff Retention and Job Satisfaction at a Hospital Clinic - a Case Study. System
Kane-Sellers, M. L. (2007). Predictive Models of Employee Voluntary Turnover in North American Professional
Sales Force Using Data-Mining Analysis. Texas: Graduate Studies of Texas A&M University.
Kunc, M. (2008). Achieving a Balanced Organizational Structure in Professional Services FirmsL Some Lessons
from a Modeling Project. System Dynamics Review Vol. 24, No. 2, 119 - 143.
Levenson, A., Fenlon, M. J., & Benson, G. (2010). Rethinking Retention Strategies: Work-Life Versus Deferred
Compensation in a Total Rewards Strategy. Los Angeles, California: Fourth Quarter WorldatWork
Linard, K. (2002). System Dynamics Modelling: HR Planning & Maintenance of Corporate Knowledge. New
South Wales: Australian Defence Force Academy, UNSW.
LLC, D. D. (2004). It's 2008: Do You Know Where Your Talent Is? London: Deloitte Consulting UK.
Lynch, K., Akridge, J. T., Schaffer, S. P., & Gray, A. (2006). A Framework for Evaluating Return on Investment in
Management Development Programs. International Food and Agribusiness Management Review Vol.
9, Issue 2.
Maister, D. H. (1997). Managing the Professional Service Firm. New York: Simon and Schuster.
Meadows, D. H. (2008). Thinking in Systems. Vermont: Chelsea Green Publishing.
Morecroft, J. (2000). Visualising and Simulating Competitive Advantage: a Dynamic Resource-Based View of
Strategy. London Business School System Dynamics Group Working Paper WP/0036.
Morecroft, J. (2007). Strategic Modelling and Business Dynamics: A Feedback Systems Approach. Chichester:
John Wiley & Sons.
Morecroft, J. W., & Sterman, J. D. (1994). Modeling for Learning Organizations. Portland, Ore.: Productivity
Mutuc, J. S. (1994). Investigating the Dynamics of Employee Participation. System Dynamics: Methodological
and Technical Issues (pp. 161 - 171). Stirling: International System Dynamics Conference.
Myrtveit, M. (2000). The World Model Controversy. Bergen: The System Dynamics Group, University of Bergen.
Price, J. W., & Mueller, C. W. (1986). Absenteeism and Turnover of Hospital Employees. Greennwich, Conn: JAI
Rivera, L. A. (2012). Hiring as Cultural Matching: The Case of Elite Professional Service Firms. American
Sociological Review, 999 - 1022.
Schoemaker, P. H. (1995). Scenario Planning: a Tool for Strategic Thinking. Sloan Management Review, 36, 25
Schoemaker, P. J. (1993). Multiple Scenario Development: Its Conceptual and Behavioral Foundation. Strategic
Management Journal, Vol. 14, No. 3, 197 - 213.
Schwartz, P. (1996). The Art of the Long View Planning for The Future in an Uncertain World. New York:
Senge, P. M. (1990). The Fifth Discipline. The Art and Practice of Learning Organization. New York:
Senge, P. M. (1992). Mental Models. Strategy & Leadership, Vol. 20 Iss: 2, 4 - 44.
Skok, M. M., Zoroja, J., & Bach, M. P. (2013). Simulation Modeling Approach to Human Resources
Management: Burnout Effect Case Study. Interdisciplinary Description of Complex Systems 11 (3), 277
Sterman, J. D. (1994). Learning In and About Complex Systems. System Dynamics Review 10 (2-3), 291 - 330.
Sterman, J. D. (2000). Business Dynamics: System Thinking and Modeling for a Complex World. McGraw-
Sterman, J. D. (2001). System Dynamics Modeling: Tools for Learning in a Complex World. California
Management Review, Summer 2001, Vol. 43 Issue 4.
Sveiby, K. E., Lynard, K., & Dvorsky, L. (2005). Building a Knowledge-Based Strategy A System Dynamics Model
for Allocating Value Adding Capacity. Boston: System Dynamics Conference.
Tabacaru, M. (2008). What We Don't Measure About Human Resources: Intangible Variables Made Tangible.
VDM Verlag Dr. Mueller e.K.