=Paper=
{{Paper
|id=Vol-2991/paper16
|storemode=property
|title=The Human Resource Management challenge of predicting employee turnover using machine learning and system dynamics
|pdfUrl=https://ceur-ws.org/Vol-2991/paper16.pdf
|volume=Vol-2991
|authors=Eya Meddeb
|dblpUrl=https://dblp.org/rec/conf/bir/Meddeb21
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==The Human Resource Management challenge of predicting employee turnover using machine learning and system dynamics==
https://ceur-ws.org/Vol-2991/paper16.pdf
The Human Resource Management challenge of
predicting employee turnover using machine learning
and system dynamics
Eya Meddeb 1
1 University of Worcester, Department of Computing, Henwick Grove, Worcester, WR2 6AJ,
UK
mede1_20@uni.worc.ac.uk
Abstract. The turnover rate is an indicator of the company situation (health), a
high turnover may indicate dissatisfaction with the work conditions, conflicts
with the management or lack of career opportunities, etc. The unexpected loss
of high skilled employees will lead to less productivity and less quality which
can threaten the company’s competitive advantage in the market. The turnover
rate is difficult to predict and challenging to manage, therefore, having a warning
system or tool that predicts possible resignations at an early stage may help HR
managers improve their retention strategies and maintain a stable rate.
The main target for this research proposal is to build a model based on HR
managers observations and knowledge alongside theoretical assumptions using
historical data to assist decision-making. The model will be built, constructed,
and developed using machine learning and system dynamics to act as a predictive
tool. Therefore, HR managers will be able to predict the turnover rate and most
importantly to identify the reasons behind it, thus, they will have more flexibility
in adjusting and improving retention strategies. To build this predictive model,
mixed methods qualitative and quantitative will be used taking into consideration
the ethical approval process of data gathering by the university of Worcester.
Keywords: employee turnover, human resources management, machine learn-
ing, system dynamics.
1 Introduction
The term ‘turnover’ means that the employee leaves the company permanently and
ends the relationship with the organization. Scholars in this field correctly defined it as
the rotation of employees around the market; between the firms, jobs, and occupations;
and between the states of employment and unemployment [1,17]. The main target for
companies is to maintain a stable turnover rate since high turnover may be considered
as an indicator of an issue with the organization [5,33].
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0)
184
A proper administration of human resources management (HRM) practices is crucial
in retaining employees in organizations. HR professionals and line managers need to
work closely to ensure all key practices such as managing performance and employee
relations are executed in an effective manner. The importance of managing human re-
sources has been growing over the past years in academia and in practice. The percep-
tions of human resources practices are more important than the actual practices in de-
veloping employee commitment. Management scholars and practitioners have exerted
continuous efforts in learning more about human resources practices and how these
practices enhance employees’ performance and achieve organizational goals [17,20].
Data mining, an effective means of obtaining useful information by identifying
trends and patterns [21], has been one of the ways to demonstrate how HRM practices
can affect the organisation. Human resources in the enterprise generate various data
which can be used in the formulation of corporate strategies and the selection of em-
ployees. In recent years, the number of research contributions that aim at supporting
the practical adoption of HRM data mining has been rapidly growing. These contribu-
tions refer to various HRM activities and processes, such as predicting and evaluating
employee performance, predicting employee turnover, etc. [35,21].
Intelligent algorithms based on machine learning (ML), a subset of artificial intelli-
gence that incorporates the principles of data mining to predict business and possible
outcomes using historical data [22,3], can help in resolving some of the mentioned
challenges as well as in increasing efficiency and effectiveness of HRM. However, or-
ganisations should be mindful that the purpose of integrating these technological capa-
bilities is not to replace humans, but rather to improve the decision making around peo-
ple [8,10].
2 Related research
Several features were highlighted in previous studies as drivers for the turnover rate
depending on the selected field, the followed methodology and the analysis methods.
The research by [5] showed that gender followed by age then education level were the
first variables affecting the turnover prediction in a manufacturing company. [37] also
indicated that gender is one of the most relevant features to consider in predicting the
turnover rate beside economic indicators such as GDP. [25] highlighted the impact of
withdrawal behaviours such as lateness and absenteeism on predicting the turnover in
a large software company. Contrary to some previous studies, the research by [33]
showed that features such as social interaction ability, age and marital status were in-
significant to predict the turnover in a manufacturing company. However, the number
of previous job changes and the knowledge about the working conditions were highly
correlated to the turnover rate.
185
For Machine learning and data mining methods, several studies conducted their re-
search to predict the turnover rate in the most accurate way combining different tech-
niques. A variety of machine learning approaches have been applied to prediction of
turnover rate. Common approaches include decision tree classification [6,41] and en-
semble learning [42,43]. However, applications of machine learning combined with
novel computational models has led to further improvements. A study by [37] proposed
a new method to predict the turnover including the time factor by combining ensemble
learning and survival analysis (statistical approach). The results proved that this com-
bination improved the accuracy of the model compared with the others machine learn-
ing algorithms. Moreover, dealing with the turnover rate from a dynamic perspective
[7] developed a predictive model using a system dynamics approach. System Dynamics
is an approach that allows the creation of micro-worlds where space and time can be
compressed and slowed, so you can experience the long-term side effects of your deci-
sions [34]. Different retention strategies were tested with this model which led to con-
clude that the fact of understanding the reasons causing high turnover may be more
important to set an effective retention strategy, taking other factors into consideration.
Having an accurate model that predicts the turnover rate can help HR managers set
a suitable retention strategy in time. Few interventions that have been proven successful
to manage the turnover are through improving the recruitment strategy [4,6], improving
the candidate selection [16,14], providing various self-development opportunities
through trainings [15], and by providing various incentives such as bonus, rewards, and
competitive compensation [40]. For example, [25] mentioned in their study that young
engineers are more interested in having experience in different domains rather than in
one specialisation. Therefore, offering a better technology or domain can attract engi-
neers more than compensation. Hence, knowing the cause of high turnover is important
to improve decision making since it is different from one field to another, and it depends
on the current situations of the company itself.
Interventions through human resource management puts its focus on two indicators
for organisations, first is the turnover rate in a period that is deemed acceptable. 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 can be the basis for evaluat-
ing the best strategy to manage turnover [7].
Based on previous research, it can be can concluded that predicting the turnover rate
is an important issue for organisations, several models were built with good, achieved
performance, but the majority was not developed starting from real-world observations
specifically causal relationships between different variables to consider while building
the model. The authors were mainly focusing on proving a good theoretical accuracy
based only on historical data. Therefore, the fact of building a model closer to reality
by starting from real-world causality, captured from interviews with HR and line man-
agers alongside literature to support the theoretical development of a predictive tool can
help HR managers analyse the turnover rate in a different way by highlighting the real
186
reasons behind it, and by testing the outcome of different scenarios/retention strategies
before taking any decision.
3 Outline of the research questions and objectives
3.1 Research questions
RQ1: What quantifiable features can be identified by HR experts, line managers and
from the literature, that are considered practical cause-effect or causal links to predict
the turnover?
RQ2: What is the most effective combination of machine learning algorithms and
dynamic/causal modelling approaches to build a predictive model based on real-world
causality?
RQ3: How can the outcomes of a predictive model be used to evaluate the efficiency
of different retention strategies?
3.2 Research Aim
The aim of this study is to provide a data-driven predictive tool to enable HR man-
agers analyse the turnover rate in a different way by capturing the most important var-
iables causing the fluctuation and testing the outcome of different scenarios. This tool
will be focused on real-world causality concluded from interviews and previous re-
search to enable HR managers take into consideration the impact of each variables in-
cluding the long-term interest of the company before taking any decision.
3.3 Research Objectives
O1:Identify the field of interest to consider in this research based on networking and
data accessibility (which industry/sectors to consider).
O2:Identify the most relevant features and the most important causal links that relate
to the turnover rate based on HR experts’ and line managers point of view, and previous
research.
O3:Select the most effective approach between system dynamics and ML to predict
the turnover based on real-world causality.
O4:Analyse the results from a theoretical (technical) perspective.
O5: Evaluate the performance of the system from a perspective of a professional
practice.
O6: Establish different scenarios to predict the turnover rate and test the efficiency
of different scenarios/retention strategies (Consider a specific use case).
187
4 METHODOLOGY
This topic will be mainly focused on predicting the future, therefore, the research
onion for future studies by [23] inspired from the original research onion by [32] will
be followed. The research onion is a way to organise and structure the research meth-
odology following consecutive layers step by step starting from the main philosophy
until choosing the techniques and procedures of data collection and analysis [32,23],
(See figure 1).
Fig. 1. Research onion for futures studies [23]
4.1 Philosophy
Usually, researchers aim to follow a positivism philosophy for prediction studies
using specific statistics and algorithms to predict one possible scenario and have accu-
rate results only from a technical perspective. For this study a more realistic model is
investigated with a focus on the employee turnover as a variable and its causality with
the other variables in a dynamic way. The main target of this topic is to predict the
turnover rate and analyse how it can be affected by other observable variables and vice
versa. Hence, multiple scenarios are considered for the future based on adjusting the
most relevant observable features correlated to the turnover in order to test the outcome
of different scenarios, taking into consideration the company’s interest. Therefore, crit-
ical realism is the right philosophy for this research [23].
188
4.2 Approaches to futures research
According to [18], two approaches may be considered forecasting and foresight. The
first approach forecasting is mainly applied in areas in which tangible quantitative data
is available, such as demography, economic development, while the second approach
foresight which leads to have a complex cognitive-analytical view of multiple futures,
is used in areas such as institutions, culture, and politics. Therefore, forecasting will be
used in this research to evaluate the outcome of each scenario by feeding the model the
required data to learn the logic of the selected scenario (causal relationships), [23].
4.3 Approaches to theory development
Abductive reasoning starts with the observation of weak signals in a specific area, it
is mainly applied to draw a conclusion from low knowledge. The fact of considering
real-world causality to predict different scenarios is still a new area in which several
theories and hypotheses are not established yet. Thus, abductive reasoning will be con-
sidered when the causal loop diagram will be developed and inductive reasoning will
be used to set general conclusions before moving to the development of a clear theoret-
ical position to build the model [27,19,23].
4.4 Strategies
Three main research strategies can be distinguished in this area: descriptive, norma-
tive, and explorative. Descriptive methods aim to have a precise description of future
events, normative methods aim to shape the desirable and undesirable future in order
to establish pathways or chain of events to reach the desirable one, and explorative
methods aim to study multiple futures and explore possible developments. This re-
search will be mainly focused on exploring different scenarios for the future based on
adjusting different features to analyse their impact on the turnover rate. Hence, explor-
ative methods will be used to investigate multiple scenarios and descriptive methods
will be used to examine and explain the observed outcomes [28,18,23].
4.5 Methodological choice
Research methods can be distinguished into quantitative methods, such as time series
analysis, causal analysis, etc, qualitative such as Delphi surveys, and mixed methods
which are classified as quantitative and qualitative such as scenario construction and
modelling. Thus, mixed methods; qualitative and quantitative will be followed in this
study to generate hypotheses then build the predictive model based on them [31,23].
4.6 Time horizon
Three basic time horizons are defined in future studies (time scale of prediction):
short-term which is up to 10 years, medium-term, up to 25 years, and long-term which
can be more than 25 years. This time horizon refers to the period that will be studied or
189
to the chronological horizon of varying breadth. For this research, short term will be
considered [18,23].
4.7 Techniques and procedures
In the last layer “techniques and procedures” , the research design will move towards
data collection and analysis. As mentioned in previous steps, mixed methods is consid-
ered, hence, qualitative and quantitative data collection and analysis will be followed
[23].
Qualitative data collection and analysis
. The main purpose for this research is to develop a robust prediction model
closer to reality and link the theoretical research to the professional require-
ment by focusing on causality between different features that are relevant to
predict the turnover rate. Therefore, two sources will be considered to identify
these features; previous research and primary qualitative data which are semi-
structured interviews with HR experts and line managers. For previous re-
search, articles and papers are selected based on their number of citations,
quality of their journals and authors, then structured in descriptive tables. For
the semi-structured interviews, several interviews will be conducted with HR
managers and line managers for a better understanding of the strongest causal
connections to consider in predicting the turnover rate based on their experi-
ence and knowledge. The number of interviews cannot be defined therefore
when the saturation in the amount of information needed is reached (no new
information is concluded from the interviews), the interviews will stop [11].
Thematic analysis will be used to have a critical review of responses by
determining appropriate coding and creating themes from those codes. This
method will be conducted during the entire interview process, which will pro-
vide structure and integrate reflexivity to the research [44,9].
Theoretical framework will be considered too, it is a structure that can hold
or support a theory of a research study by introducing and describing the the-
ory that explains why the research problem under study exists [2,39]. Hence,
it will be used to establish hypotheses based on causality around the turnover
rate from interviews and previous research.
Causal loops diagrams summarizing the findings from the interviews along-
side previous research will be developed to be integrated later in a predictive
model (causal or a cause-effect relationship, it depends on how much details
we can capture from interviews and previous research). A field will be selected
when building the causal loops diagrams such as IT, Higher education, etc,
and a precise use case (an industry in that field) will be investigated in the
quantitative part.
190
Quantitative data collection and analysis
. In this study, a hybrid approach combining the logic of the system dynam-
ics methodology (dynamic causality) and ML algorithms in the context of pre-
dicting the employee turnover will be investigated.
Solutions will be represented in the form of a larger graph of causal loops
inspired from the first step of system dynamics [34], different sets of non-
linear dynamic equations can be tested. Causal-comparative research method
will be considered to analyse the results. This quantitative method is used to
identify a causal relationship between an independent variable and a depend-
ent variable. The relationships between dependent and independent variables
are usually suggested (not proven) because the researcher does not have a
complete control over the independent ones [38,29].
A machine learning approach will be followed to optimise this graph rep-
resentation of the dynamic causality or cause-effect relationships. This ap-
proach will allow prediction of the turnover rate and enable analysing the rea-
sons causing the turnover to be high or low. Secondary data from an HR de-
partment (individual organisation) will be gathered at this stage to analyse a
precise case. The standard HR datasets provided by IBM and Kaggle can be
used too for technical testing and analysing [12,13].
This combination of data- and knowledge-based solution by integrating
causality in the model will allow HR managers to have more than one scenario
to manage the turnover rate by adjusting different variables based on the com-
pany interest [30].
The CRISP-DM methodology will be followed to build the model, it is a
reference that can provide an overview of the life cycle for data mining pro-
jects. It includes six sequential phases, shown in Figure 2 [36].
191
Fig. 2. Phases of the Current CRISP-DM Process Model for Data Mining [36]
Fig. 3. Research methods framework
192
5 Expected contribution
This research contribution will be a confirmation, replication of a theory within the
area of employee turnover prediction, precisely, confirming the validity of applying a
graph representation approach to combine the logic of the system dynamics methodol-
ogy with machine learning algorithms, to the prediction of turnover rate focused on
real-world causality.
6 Stage of the research
At this stage of the research, the semi-structured interviews are still in progress with
HR managers and line managers. As mentioned earlier , thematic analysis is considered
during the entire process of the interviews. Moreover, causal loops diagrams are devel-
oped using VENSIM PLE based on observations throughout data collection and analy-
sis. Purposeful and snowballing sampling have been considered in selecting partici-
pants: Purposeful sampling is a technique widely used in qualitative research for the
identification and selection of information-rich cases for the most effective use of lim-
ited resources. This involves identifying and selecting individuals or groups of individ-
uals that are especially knowledgeable about or experienced with a phenomenon of in-
terest which is employee turnover in this case. It is highly subjective and determined to
generate the qualifying criteria each participant must meet to be considered for this
research study [26]. The most important criteria to select participants is being an HR
manager or a line manager for at least two years. In Snowball sampling, the existing
study subjects recruit future subjects among their acquaintances. Sampling continues
until data saturation is reached (no new information concluded or added from the inter-
views), [24].
7 Acknowledgment
Supervisors:
Dr Christopher Bowers, University of Worcester, Department of Computing , Henwick
Grove, Worcester, WR2 6AJ, UK, c.bowers@worc.ac.uk.
Dr Lynn Nichol, University of Worcester, Department of Management & Finance, Hen-
wick Grove, Worcester, WR2 6AJ, UK, l.nichol@worc.ac.uk.
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