Research
Information
Available online at www.researchinformation.co.uk
The Journal of Grey System
Volume 32 No.1, 2020
Scaling Foreign-Service Premium Allowance
Based on SWARA and GRA with Grey Numbers
Moses Olabhele Esangbedo1, Sijun Bai12,
1.School of Management, Northwestern Polytechnical University, 710072,
Xi'an, P.R. China.
2.Yangtze River Delta Research Institute of NPU, Northwestern Polytechnical
University, Taicang, Jiangsu, 215400, P.R. China.
Abstract
International companies need to compensate expatriates in relative
proportions to the sacrifices they make to encourage them to accept overseas
assignments in countries with harsh working conditions. The scaling of the
foreign-service premium allowance problem is addressed as a multi-criteria
decision-making problem. This paper presents a unique application of Grey System
Theory to the compensation and benefit section of human-resource management.
Firstly, this paper presents a hierarchical diagram to evaluate a company’s
overseas branches for scaling the compensation of expatriates. Secondly, an
unconventional hybrid method for group decision-making with uncertainty is
presented. The hybrid method, Stepwise Weight Analysis Ratio Assessment
weighting method and the Grey Relational Analysis with grey numbers, is applied
to scale the foreign-service premium allowance and rank overseas branches of a
company. The research results obtained are from a case study of the solutions to an
international company, which was satisfying for both top management and staff
union.
Keywords: Compensation and Benefits; Multiple criteria Decision Making;
Human-Resource Management; Grey System Theory; Stepwise Weight
Analysis Ratio Assessment; Grey Relational Analysis.
1.Introduction
Compensation
Benefits
Multicriteria
38
For companies to increase their profit by enlarging their market size, one
approach used by some domestic companies is to strategically transition to a
multinational, and possibly global, company[1]. At the initial stage of globalisation,
foreign staff need to be adequately trained to function in the overall strategy.
Sending expatriates to overseas branches can be a cheaper option to mobilising all
foreign staff to the headquarters. It reduces cost because as it minimizes the
difficulty in integrating all local staff into the headquarters when the they speak a
different language, and the technology equipment is also in a different language,
 Corresponding Author: Sijun Bai. School of Management, Northwestern Polytechnical University,
710072, Xi'an, P.R. China; Email: baisj@nwpu.edu.cn
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
such as Chinese. One of the best options, at a relatively reduced cost, is to send
expatriates to these overseas branches to train foreign staff, operate the company’s
equipment, and transfer the company culture[2]. As the number of branches increase
globally, one observes that the business environment differs from country to
country, and one of the most pressing challenges that companies and
human-resource (HR) departments face in their international-mobility processes
becomes the definition of compensation and benefits (C and B) policies [3].
Generally, expatriates would want to be fairly compensated, i.e., a reasonable
allowance for working overseas, especially in remote, intimidating, or dangerous
locations[4].
In this paper, foreign-service premium (FSP) allowance refers to the lump sum
besides other benefits given to expatriates as a compensation for working overseas
to attract, retain, and motivate them. Some companies may refer to this as hardship
allowance or expatriate allowance. This allowance can be considered as payment to
expatriates to compensate them for accepting an assignment overseas because of
the different culture, work environment and distance from family. Naturally,
expatriates feel they should be differently compensated when they accept
assignments in underdeveloped countries than assignments in developed
countries[5]. Setting a different level for premium allowance based on location is
what we refer to in this paper as scaling. The objective of scaling this allowance is
to create fairness, which is the bedrock for staff performance, and to encourage
expatriates to accept an assignment in an unpleasant location [4]. This research does
not cover the total C and B package, such as the salary scale for expatriates.
Scaling FSP allowance can be addressed as a multi-criteria decision-making
(MCDM) problem[6]. For one to be able to evaluate these locations for foreign
assignments, various factors must be taken into consideration, which are the criteria
for assessment. This research is grounded on the grey system theory (GST), which
can deal problems with poor information[7,8]. The conditions in these locations
consist of uncertainties that should be accounted for, and these uncertainties are
captured using grey numbers (GN). The degree to which one criterion is more
important than the other is estimated based on the rankings and comparative points,
given by a group of decision-makers (DMs), and they are used to estimate the
weights of the evaluation criteria. The criteria rankings and scores are aggregated
using the stepwise weight analysis ratio assessment (SWARA) method[9], and
traditional grey relational analysis (GRA) with GN is used to provide ratios for
scaling FSP allowances for different locations.
This paper provides two contributions. Firstly, a simple hierarchical diagram
for evaluating the location of overseas branches in different countries is presented.
Secondly, a hybrid method that combines SWARA and GRA using GN for solving
MCDM problems in uncertain environment is presented. The rest of the paper is
organized as follows: the section 2 gives the literature review with some related
works, the section 3 is the methodology used in this research, the section 4 is the
results and analysis of this research with a real-world case study, and a conclusion
is drawn in the section 5.
2.Literature Review
Generally, MCDM problems could be considered from the aspect of weighting
and evaluation approaches. In the 1970s, Dawes and Corrigan [10] proposed a
solution for unknown weights called the equal weights (EW), and they argued that
equal weights produce the optimal result. However, when compared with methods
Compensation
Benefits
Multicriteria
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Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
later developed, EW produced the worst result. Other methods such as the
rank-sum (RS), rank-reciprocal (RR), and rank order centroid (ROC) weights [11] [12]
were solely objective methods. This led to subjective methods based on points
allocation as well as pairwise comparison approaches such as the analytical
hierarchical process (AHP)[13] and simple multi-attribute rating technique
SMART[14] method. Although the SWARA method combines both objective and
subjective approaches, it does not consider uncertainty. Also, simple common
evaluation methodologies in the literature do not consider uncertainty, for instance,
simple additive weighting (SAW)[15,16], weighted aggregated sum product
assessment[17], and ELECTRE[18] (French phase: elimination et choix traduisant la
realité, which means elimination and choice translating reality). Thus, a
predominant approach addressing this limitation is the use of hybrid MCDM
methods to consider uncertainty in decision-making. In this paper, the realities of
uncertainty are not ignored but addressed by applying the GST in a group
decision-making problem.
2.1 GST With Some Application
The Grey System Theory (GST) was introduced by Professor Deng Julong,
the father of GST [19], in the 1980s. It is primarily developed by the Institute of
Grey System Studies[20]. GST is mainly used to solves the problems that consist of
unknown factors, and it is widely used in agriculture, geology, meteorology,
engineering and other disciplines. GST is applied to study problems with few data,
small samples, inadequate information, partially known information, and an
uncertainty decision environment. Some advantages of the GST are: it does not
conform to a particular kind of data distribution, and membership function of the
data is not needed, as in the case for fuzzy numbers.
Hybrid grey methods for solving problems in the manufacturing industry has
been proposed by researchers. Wang et al.[21] proposed the design of the experiment
and the GRA method for strategy selection in the manufacturing industry. Bai and
Sarkis[22] applied a three-parameter interval grey to integrate the neighbourhood
rough-set theory and cumulative-prospect theory for evaluation and ranking. The
proposed hybrid method was to evaluate advanced manufacturing technologies by
considering the environmental regulation that contributes to improving grey
flexibility. Wang et al.[23] applied a combination of the simple additive weighting
(SAW), technique for order preference by similarity to the ideal solution (TOPSIS),
and GRA methods in selecting facilities location to improve efficiency in
manufacturing. Sometimes, outsourcing may be cheaper than manufacturing.
Kabak and Dagdeviren[24] proposed the ANP and GRA hybrid method for solving
the computer numerical control (CNC) router machine selection problem, where
ANP and GRA are used for weighting the criteria and ranking the machines,
respectively. Clean energy and production are essential to save our planet from
environmental degradation. Zhang et al.[25] selected the optimal green supplier for
the production of rubbish bins using a hybrid of the DEMATEL, AHP, and GRA
methods. Tseng et al.[26] proposed a hybrid of interval-valued triangular fuzzy
numbers, GRA weighting and the Fuzzy Delphi ranking method to evaluate green
supply-chain management in a Taiwanese electronic-production focal firm.
Newer hybrid MCDM method by researchers have been developed. Li and
[27]
Zhu
presented a grey relational decision-making model using three-parameter
Compensation
interval
grey number based on the AHP and Data Envelopment Analysis (DEA).
Benefits
Here, the AHP and DEA are used to determine the weights of the criteria that are
Multicriteria
used in the three-parameter interval grey number. This approach was applied in
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Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
analysing aircraft carrier, and can also be applied in other industries such as
agriculture. Yuan [28] presented a green agricultural structural optimization model
based on GRA with an optimization function that improved the evaluation result
significantly. Kumar et al.[29] analysed and optimized the rolling process using
carbon tools and steel based on GRA. Suvvari et al.[30] evaluated the performance
of 24 life insurance companies in India by using capital adequacy, liquidity,
operating, and profitability ratios as the evaluation criteria. Then, the traditional
GRA was used in ranking the insurance companies based on their grey relational
grades. Zhang and Yuan[31] applied GRA and provided a guide in setting up of a
scientific system for college student education. Esangbedo and Bai[32] proposed the
grey regulatory focus theory weighting method and applied it in evaluating
university reputation based on GRA.
Furthermore, Darvishi et al.[33] presented a comparative analysis of the grey
ranking approaches and suggested that the kernel degree and degree of greyness is
method provides more benefit than other methods such as the grey possibility
approach. Xi and Wei,[34] after selecting the invariant degree of greyness and kernel
normalization method, introduced Consistency Coefficient, and obtained the
optimal scheme for ranking the alternatives. Gou et al.[35] formulated a multi
attribute grey target decision-making based on the kernel and double degree of
greyness that maintained the properties of the three-parameter interval grey number.
Wang and Hu[36] integrated a genetic algorithm with a multivariate grey prediction
model that improved pattern classification by incorporating a temporary order to a
time series in the classification process. Dang and Zhang [37] proposed a grey
clustering model that is centered on the kernel and information field as a
whitenization weight function. The drought natural disaster risk in Henan province
was analysed using the grey and fuzzy clustering model, indicating five factors and
three classes of the risk. Dang et al.[38] established a two-stage grey cloud clustering
model to analyse the possibility of drought in Henan province using the coefficient
vector of kernel clustering. The results from the research divided Henan provinces
into five categories.
GRA is been applied in provincial and national problems. Bao et al. [39]
evaluated the industrial structural upgrade of Anhui province using the GRA and
showed the industrial structure have been increasing for a period of 10 years,
indicating Anhui is rapidly moving towards the post-industrial era. Xiong and
Xiong[40] utilized Driving force, Pressure, Status, Influence, Responds (DPSIP)
model combine with GRA to analyze the ecologically sustainable development and
dynamic forecasting in Heifei, China. Their research results suggested, there would
be continuous growth in the development of the province with respect to
sustainability. Tang and Xie[41] constructed a clustering evaluation model that used
a mixed possibility function for assessing tourism development potential. The AHP
was used to estimate the weights of the criteria in Huangsha city. Yin et al.[42]
examined the characteristics of the grey relational degree of proximity using
weighted mean distance, and induced intensity was applied in analysing the total
water consumption in China, which is correlated with agricultural and industrial
water usage. Hu et al. [43] evaluated the air quality of 74 cities in China by
integrating the pollution indexing systems and using the grey fixed weight
clustering analysis model that amounted to a comprehensive pollution Compensation
measurement and control strategy. Liu and Cheng[44] analysed the good
Benefits
transportation volume and GDP in China’s port from the year 2002 to the year 2017
Multicriteria
and reported that Metal ore is the biggest contributor to China’s port transportation
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Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
volume and GDP.
Furthermore, beyond China, Aydemin and Sahin[45] applied GRA for
evaluating the healthcare service quality and the factor affection the satisfaction of
patients in Turkey. Pitgatto et al.[46] analysed the 24 Brazilian food companies in
Sao Paulo state to identify the essential factor, which was ranked using the GRA.
Sheikh et al.[47] applied GRA in evaluation factors influencing the process quality
in a construction project in Pakistan. Tawiah [48] et al. applied GRA in evaluating
the impact and control of malaria in the Sub-Saharan Africa from the year 2010 to
2017. Esangbedo and Che[49] evaluated the business environment in Africa by
combining the GRA and rank order centroid weights.
Last but not least, the grey system theory not only can be used in evaluating the
past and present, but it also can be used in the prediction of the future. Liu et al. [50]
proposed a grey Army Materiel System analysis activity (AMSAA) model that is
combined with the GM(1,1) model for increasing the consistency in the evaluation
of the flight testing phase of large civilian aircraft. Liu et al.[51] presented the use of
a reclusive GM(1,1) model for forecasting the cost in the management of weapon
equipment. Wu et al.[52] extended the classical GM(1, n) model proposed a
multivariate fraction grey model, GM(α, n), that produced an accurate forecast of
the total energy consumption of China for economic and urban development.
2.2 Hybrid MCDM Methods Applied in HR
Compensation
Benefits
Multicriteria
42
Several grey hybrid methods have been developed in the literature in the
context of human resources (HR) management. Zolfani and Antucheviciene[53]
presented a framework to select an employee by applying an analytic hierarchy
process (AHP), and the technique for order preference by similarity to the ideal
solution (TOPSIS) with grey relations for weighting and ranking employees.
Although SWARA was first applied in litigation [9], Dahooie et al.[54] used a hybrid
SWARA and grey additive ratio-assessment method in analysing the competency of
IT staff. They concluded, in these changing times, that it is increasingly necessary
to understand what influences the performance of people at work. Hence, the
importance of the IT staff resulted in organisational development as a diagnostic
tool, since it allowed the identification of what was failing and taking up what was
being done well to be able to manage favourable changes, in which HR has a
leading role. After employees are selected, there is a need to evaluate their
performance on the job. Duman et al.[55] presented a balanced scorecard-based
approach combining the DEMATEL and ANP methods for staff performance
evaluation, where GN are used in constructing a direct relational matrix.
More importantly, in this journal, researchers have extended and applied GRA.
For instance, Wang et al.[56] based the design of the capturing customer
requirements on GRA. A customer’s assessment utility, a triangular fuzzy number,
is evaluated using GRA. Li et al.[57] evaluated the work efficiency and medical
quality of a hospital in China that is based on the public–private partnership model.
Peng and Shen[58] developed an evolutionary algorithm based on GRA, which was
integrated in a linear programming solver as a local search for solving the
crew-scheduling problem. Li et al.[59] presented a comparative result on the
effectiveness of the Internet of Things between some regions in China. Khuman [60]
proposed the grey natural language processing by applying GRA for natural
language processing. Wang et al.[61] optimized cab suspension using GRA as a
parameter of a self-dumping truck. Lin and Hu[62] applied GRA in the measurement
of the similarity between two patterns that incorporate a tolerance rough set based
on an accumulated generating operator. Huang et al. [63] improved the test method
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
for the grey relational order based on grey relational grade and probability
distribution. Hu et al.[64] developed an aggregation-function-based similarity
measure, which can also be used for prediction. Es et al. [65] developed the
GRA–TRI for a multi-criteria and decision-aid classification method that
performed better than the ELECTRE–TR–Central. Zhu et al.[66] modified the
variable weight-clustering method to address the problem of a continuation
coefficient that can be extremely big.
Other hybrid MCDM methods with SWARA weighting methods have been
proposed by some researcher. Zarbakhshnia[67] developed the fuzzy SWARA
method that was combined with the COPRAS-G method. The linguistic triangular
fuzzy number is used to measure the opinion of the decision-maker (DM) before
integrating it with the SWARA method for the evaluation of a logistic provider.
Mardani et al.[68] presented a systematic literature review on the SWARA and
WASPAS methods as, in 2016, the numerous hybrid method with SWARA for
uncertain decisions was integrated with the fuzzy set theory. However, the reported
SWARA and COPRAS-G hybrid method by Gholamreza et al.[69] only reported the
estimated weight, and uncertainty was not captured in weights using grey numbers.
Hashemkhani et al.[70] extended the SWARA method by applying the
criteria-prioritization process in the estimation of the weights; overall weights are
represented in white numbers that may not be sufficient to represent the reasonable
slack in weight that would capture uncertainty. Although some researchers may use
grey linguistic variables to measure the preference of the DMs, it should be noted
that they either used white or fuzzy numbers as the criteria weights for evaluation,
and not grey numbers directly, with the exception of Chithambaranathan et al.[71].
2.3 Compensation and Benefit in Decision-Making
Compensation and Benefit (C and B) can be described as all monetary
payment and welfare that employees receive for their work. Direct compensation
may be in regular intervals as wages, salaries, bonuses, and commission. Indirect
compensation includes all monetary payment that is excluded from direct
compensation that is deemed to be part of the social contract between employer and
employee, such as benefits like leave with pay, insurance, pension plans, training,
and services for employees. Nonmonetary benefits refer to factors such as a career
path/career prospects, opportunities for recognition, and a good environment and
working conditions. We recognize that the factors that attract employees to a
company can be different from those that keep them in a company. According to
Highhouse et al.[72], the challenges to HR management involves managing and
monitoring the work environment, organisational values, competencies,
commitment to the mission, motivational quality, level of training, and career plans.
Their research showed that a company’s attractiveness and prestige are different
constructs.
Unarguably, employees who are unsatisfied with their job and pay have low
retention possibility. Omar and Ogenyi[73] investigated the pay satisfaction of senior
managers in the Nigerian civil service, and significant determinants of satisfaction
with pay-incentive schemes were instrumental perception and procedural. The
investigation concluded that pay incentive has a dimension of pay satisfaction
supported by justice. Schaubroeck et al.[74] studied under-met expectations and
showed that pay-for-performance is related to employees’ reaction, such as the Compensation
happiness derived from a pay raise, the level of pay satisfaction, and turnover
Benefits
intention. Staff with over-met expectations are related to the merit pay-raise
Multicriteria
construct. One’s expectation has a relationship with their emotional stability.
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Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
Shrader and Singer[75] analysed the compensation of small-business managers in
China and the United States of America (USA) using the Big Five Personality Test
and pay-satisfaction questionnaires, and found emotional stability to be the major
factor for salary satisfaction. They concluded that employees’ compensation should
be well-communicated in order to justify and validate their pay level and structure.
On the contrary, the impact of pay secrecy on employee task performance has
been
researched.
Bamberger
and
Belogolovsky[76]
generated
a
moderated-mediation model to know the individual risks of pay secrecy and its
performance. On the one hand, it was discovered that pay secrecy is associated
with a high level of performance than being open with pay, i.e., perceptions about
fairness mediate pay secrecy and employee tasks. On the other hand, secrecy in pay
may have negative impact on the performance of staff who are sensitive to
inequality. Jawahar and Stone[77] also confirmed that informational justice relates to
pay-level satisfaction, pay structure and administration, as well as potentially
relating to an increase in payment. Shen[78] presented a model for Chinese
expatriate compensation that can primarily be a host-, contract-, or diplomat-based
approach, which are dependent on firm-specific factors, and host contextual factors
as well as International HRM policies and practices.
Some organisations have their workers compensated that grant bargains for
injuries, and have considerations for women. Employees whose work involves a
certain degree of physical difficulty are prone to accidents in their jobs. Employees
in the USA are protected by the law regardless of their condition or nature of
employment, where a complete benefit is provided to them[79]. Spieler[80]
investigated the rights of the disabled and analysed compensation for work injuries
in the USA in the period 1900–2017. They discovered that many workers fall into
poverty categories due to their work-caused injuries and illnesses, and that is what
worker C and B are meant to resolve. Shortland [5] used a triangulated
qualitative-research approach to know how women’s decision to be an expatriate is
affected by C and B in the oil and gas industry. The author concluded that housing
quality, salary increment, quality education for their kids, access to quality
healthcare, and travel and leave arrangements are some of the things that women
consider before they go overseas.
From the above-cited studies, and our searches in common academic-citation
databases, we identified that not enough research has been done to evaluate the
location of overseas assignments, as well as nonspecifically provided a scale to
compensate and motivate expatriates to accept assignments at different overseas
branches. This paper also fills this gap in the literature by proposing a quantitative
decision-making approach in expatriate compensation, and the use of the MCDM
method for the evaluation of overseas branches, which are objective rankings based
on the preferences of some expatriates. It is evident that there are several
applications of the GST hybrid method in the literature, and a hybrid that combines
SWARA and GRA using GN is unique because of consideration for uncertainty in
weighting and evaluation are considered.
3.Methodology
Compensation
Benefits
Multicriteria
44
The problem this paper addresses is evaluating the various locations of
overseas branches and assigning varying FSP allowances to promote fairness and
encourage local staff to take up overseas assignment at remote areas. In this section,
we define the criteria for scaling the FSP allowance, and the weighting and
evaluation methods. In other words, there was high turnover rate of expatriates as
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
the data suggests. The data for all these criteria were obtained as secondary data
from the World Bank, the World Health Organization (WHO), and other
research-institution databases. Most of the data sources for the criteria for
evaluating the alternatives for this research are from international government
organisations and their agencies, as well as research institutions. The SWARA
weighting method was extended for group decision-making with GN to estimate
the weights of the criteria. Meanwhile, GRA using GN was used to evaluate the
alternatives.
3.1 Evaluation Criteria
The hierarchy structure for evaluating the location of overseas branches
consists of five first-level criteria, 15 second-level criteria, that is, three
second-level criteria for each first-level criteria. The first-level indicators are
measured as a formative construct, while the second-level criteria are measured as
a reflective construct because they are conceptually correlated. Since the
second-level of each criterion is correlated, even more than three second-level
criteria amount to the same approximation of rates in grey numbers. The major
reason for the criteria used for evaluation is the availability of data at the evaluated
branches, and the resources used in obtaining the data. Figure 1 shows a
hierarchical structure for scaling FSP allowance. The criteria are defined as
follows:
1) Natural Environments (C1): This consist of the Clean Cities (C1-1), the
Environmental Performance Index (C1-2), and the Disaster Risk Index (C1-3). Clean
Cities (C1-1) is a measure of the annual mean concentration of fine particulate
matter of less than 2.5 microns of diameter (PM 2.5) (ug/m 3) in a country’s urban
areas. Air pollution can expose individuals to health risks[81]. The Environmental
Performance Index (C1-2) is a ranking of 180 countries that covers the quantitative
metric of pollution control and the management of natural resources, which
includes the environmental-health and ecosystem-vitality categories of 24
indicators[82,83]. Disaster Risk Index (C1-3) captures the kind of natural disaster that
can overpower the capacity of a nation to respond. Data used in this indicator are
the natural categories of the hazard and exposure dimension, which consist of
earthquakes, tsunamis, floods, tropical cyclones, and droughts [84–87].
2) Conflicts State (C2): This consists of three indicators, the Global Terrorism
Index (C2-1), Failed State Index (C2-2), and Global Peace Index (C2-3)[88]. The
Global Terrorism Index (C2-1) is analysis of the impact of terrorism in 163 countries,
with about 99.7% of the world’s population being covered [89]. The database
records of terrorist incidents and death toll are maintained by the Institute for
Economics and Peace (IEP) that are used in C2-1. The Fragile State Index (C2-2) by
the Fund For Peace (FFP) organisation provides the rankings and scores of 178
countries by using quantitative and qualitative data with expert validation to
promote security and prevent violence[90]. The effort to maintain peace in a country
is also used as a criterion, i.e., the Global Peace Index (GPI) (C2-3) by providing
quantitative data to measure peace that has some relationship with the prosperity of
a country, as well as promoting cultural understanding in the world[91]. The GPI is
based on domestic and international conflict, the safety and security level in the
society, and militarization based on funding and access to weapons.
3) Economy Performance (C3): This indicator measures the economy of Compensation
countries, as this may affect expatriates. It consists of the Consumer Price Index
Benefits
(CPI) (C3-1), Gross Domestic Product (GDP) per Capita (C3-2), and Inflation (C3-3).
Multicriteria
The CPI (C3-1) uses a base period of 2010 to depict the fluctuations in the cost for a
45
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
typical consumer of annually buying a basket of goods and services [92]. GDP per
Capita (C3-2) is the GDP divided by the midyear population. The GDP is the gross
value of all the goods and services produced by a country, with all subsidies
excluded[93]. Inflation (C3-3) is the percentage change in the cost of annually buying
a basket of goods and services for a typical consumer [94].
Clean Cities
(C1-1)
Environmental
Performance Index (C1-2)
Natural
Environment (C1)
Disaster Risk Index
(C1-2)
Global Terrorism Index
(C2-1)
Fragile State Index
(C2-2)
Conflict State (C2)
Global Peace Index
(C2-3)
Consumer Price Index
(C3-1)
GDP per Capita
(C3-2)
Economic
Performance (C3)
FSP Allowance
Scale (ri)
Inflation
(C3-3)
Sanitation and Hygiene
(C4-1)
Mortality From
Environmental Pollution
(C4-2)
Health Care (C4)
Drinking Water
(C4-3)
Public Integrity Index
(C5-1)
Justices System
(C5-2)
Regulatory
Institution (C5)
Reliability of Police
Service (C5-3)
Figure 1. Hierarchical diagram for expatriate compensation.
Compensation
Benefits
Multicriteria
46
4) Healthcare (C4): This consists of the indicator provided by the WHO for
Sanitation and Hygiene (C4-1), which reflects essential sanitation services[95].
Mortality From Environmental Pollution (C4-2): the mortality rate that is attributed
to unintentional poisoning through ambient pollution in the household and ambient
air pollution[96]. Drinking Water (C4-3) is the number of individuals in the
population that have basic and safe water services within a 30-minute walking
distance[97].
5) Regulatory Institutions (C5): Public Integrity Index (C5-1) measures the
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
capacity of a country to control corruption and ensure that public resources are
spent without corrupt practices, which includes quantifying judicial independence,
administrative burden, trade openness, budget transparency, citizenship
(electronically), and freedom of the press[98]. Justices System (C5-2) is the World
Just Project—Rule of Law Index that provides data for 113 countries that adhere to
the rule of law from the perspective of people based on their experiences[99].
Reliability of Police Service (C5-3) is the extent to which the police force can
enforce law and order in a country, based on a World Economic Forum survey[100].
3.2 SWARA Weighting Method for Group Decision-Making
The SWARA weighting method was developed to add the degree to which
criteria are ranked to each other. The SWARA method improves on conventional
ranking-weight methods, such as the rank-order centroid, rank exponent, and
rank-sum weighting methods. The step for estimating the criteria weights for using
the SWARA are as follows:
Step 1. Rank the criterion based on its level of importance. Rankings are based on
the preferences of the DMs.
Step 2. Determine the comparative importance of average value. The comparative
importance is the relative importance of criterion j in relations to criterion (j-1),
which begins with the second-ranked criterion.
Step 3. Determine the comparative coefficient. Coefficient kj is obtained using
Equation (1):
j =1
 1

,
(1)
kj = 
s
+
1
j 1

 j
where sj is the comparative importance of average value [9].
Step 4. Recalculate the weights. The recalculated weights are simply unscaled
weights q j :
 1

q j =  k j −1
 k
 j
j =1
j 1
.
(2)
Step 5. Calculate the weights. The weights are scaled to one unit. The scaled
weight relative to each other is:
qj
.
(3)
wj = n
q
 k
k =1
Now, we present an extension of the SWARA for group decision-making that
represent the DM weights with GN. GN captures the uncertain weight by
computing the weights for each of the DMs and taking the scaled minimum and
maximum weights for each criterion from the DMs.
For a weight matrix W of p DMs and n criteria,
w1n 
 w11 w12
w
w22
w2 n 
21
，
(4)
W =




Compensation
wpn 
 wp1 wp 2
Benefits
the grey weight is
W = ( w1 w2
wn ) ,
(5)
Multicriteria
47
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
 min w
max wij 
ij
1i  p

.


w j = w j , w j =
, 1ni  p

  n max w

max
w


ij
ij
j =1 1i  p
 j =1 1i  p

[54]
It should be noted that Dahooie et al.
approach of the SWARA method
results to crisp weights, i.e. white weights that do not capture uncertainty. The
implementation of SWARA method in this paper estimates the evaluation weights
as interval grey numbers that provide reasonable slack to capture uncertainty.
where
3.3 GRA Ranking Method Using GN
GRA is an important part of the GST, and uncertainties are represented as GN.
Classical GRA compares a weighted normalized decision matrix to a reference
alternative, and grey relational grades are used in ranking alternatives [101]. Although
interval numbers and interval grey numbers have apparently the same concept, they
are inherently different. While interval numbers are all possible numbers within a
range, an interval grey number is a single number within a range. The GRA ranking
method using interval GN is a modified version of the traditional GRA method.
The steps for using the GRA with GN are as follows:
Step 1. Construct a decision matrix. The decision matrix is constructed from
the raw data based on the criteria and performances of the alternatives.
 x1 (1)

x (1)
X = 2


 xm (1)
x1 ( n) 

x2 (n) 


xm ( n) 
x1 (2)
x2 (2)
xm (2)
(6)
where xi(k) are the precise data of the kth criteria for the ith alternative, 1 ≤ k ≤ n,
1 ≤ i ≤ m, and u and n are the numbers of alternatives and criteria, respectively.
Step 2. Normalize the decision matrix. This step is to make the preference
unidirectional and evenly distributed in the range of 0 to 1.
For benefit preferences, i.e., when larger values are better values, we use
Equation (7):
xi* (k ) =
xi (k ) − min xi (k )
1 k  n
max xi (k ) − min xi (k )
1 k  n
.
(7)
1 k  n
For cost preferences, i.e., smaller values are better values, we use Equation (8):
xi* (k ) =
max xi (k ) − xi (k )
1 k  n
max xi (k ) − min xi (k )
1 k  n
.
(8)
1 k  n
Thus, the normalized data matrix is:
 x1* (1) x1* (2)
x1* (n) 
 *

*
x (1) x2 (2)
x2* (n) 
X* = 2
.
(9)


 *

*
xm* (n) 
 xm (1) xm (2)
Step 3. Construct the grey decision matrix. The decision matrix is constructed
from the normalized data matrix.
Compensation
Benefits
Multicriteria
48
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
  x1,1  x1,2

 x2,1  x2,2
X = 


  xm ,1  xm ,2
where
 x1,n 

 x2,n 
,
(10)


 xm ,n 
and jth first-level criterion
 xij =  xij , xij  =  min ( C j −k ) , max ( C j −k ) 
1 k  h
1k h

has hth second-level criteria
as its last term for the ith alternative.
C j −h
(
(C )
j
)
Step 4. Calculate the weighted normalized grey decision matrix. The weight can be
obtained using any of the MCDM weighting methods in the literature. The
weighted normalized decision matrix ( X  ) is the matrix multiplication of the
normalized decision matrix (X*) and the transposed weights matrix (W) of the
criteria. The SWARA weighting method is used as the weighting method in this
research.
 X  =  X *  W 
(11)
W = (  w1 ,  w2 ,...,  wn ) .
(12)


 x2,1
  x1,1
 

 x2,1  x2,2
X  = 


  xm ,1  xm ,2
 x1, n 

 x2, n 
.


 xm , n 
(13)
That is,  xk ,h =  xk*,h  wh . In vector form, the series can be written
as:
 ,  x1,2
 ,...,  x1, n 
 X 1 =  x1,1
 ,  x2,2
 ,...,  x2, n 
 X 2 =  x2,1
.
 X m =  xm ,1 ,  xm ,2 ,...,  xm ,n 
Step 5. Determine the reference alternative.
 X 0 = { x0，1 ,  x0，2 ,...,  x0, n }
where
(14)
 x0 j =  max xij , max xij  .
1i  m
 1i m

Step 6. Determine the series differences. The difference between the reference
alternative and others are calculated to obtain the difference.
 ij =  x0 j −  xij
(15)
(
)
= max x 0 j − xij , x0 j − xij .
Step 7. Calculate the Grey Relational Grades (GRG). The GRG (ri) is
calculated from the grey relational coefficient (  ) of the alternatives using
Compensation
the following formula:
Benefits
1 n
ri =  j =1 ij ,
(16)
Multicriteria
n
49
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
where  is the distinguishing coefficient, and the grey relational coefficient is:
min min ij +  max max ij
 ij = 1im 1 j n
1i  m 1 j  n
.
ij +  max max ij
(17)
1i  m 1 j  n
4.Result and Analysis
This section presents a case study of scaling FSP allowance in the
petroleum-equipment manufacturing and service industry located in China. The
company globally has 22 branches in 22 countries. It was observed that some staff
did not want to work in very remote branches. Thus, the proposed method was
applied to solve this problem. Alphabetically, the branches evaluated are located in
the following countries: Albania, Algeria, Bangladesh, Brazil, Canada, Colombia,
France, Indonesia, Italy, Kazakhstan, Malaysia, Mexico, Nigeria, Pakistan, Peru,
Poland, Romania, Russia, Ukraine, United Arab Emirates (UAE), USA, and
Venezuela. Also, the data used in this research were collected in the third quarter of
the year 2018.
4.1 Criteria Weights
Four expatriates (DM1, DM2, DM3, and DM4) with over 70 years of
cumulative work experience were requested to give their rankings and the
comparative points to all the criteria. Details about the DMS remains anonymous so
that they will be untraceable. The rankings by the DMs are given in Table 1.
Table 1. Raw rankings of the first-level criteria by the decision-makers (DMs)
Expatriates (DMi) /
DM1
DM2
DM3
DM4
First-level Criteria (Cj)
Natural Environment (C1)
2nd
2nd
3rd
4th
Conflict State (C2)
Economic Performance (C3)
Health Care (C4)
Regulatory Institution (C5)
1st
1st
1st
1st
4
th
3
rd
2
nd
2nd
3
rd
5
th
4
th
5th
5
th
4
th
5
th
3rd
These ranking were used to estimate the weights of the criteria based on the
SWARA weight method in Section 3.2. For DM1, computation is shown in Table 2.
The computation for the other DMs is omitted. The weight estimation by all DMs
is shown in
Table 3, and weight matrix W is obtained using Equation (4) from Table 3.
Based on Equation (5), the grey weights are:
W = ([0.1744, 0.2022], [0.2605, 0.2769], [0.1295, 0.2131], [0.111, 0.1665],
[0.107, 0.1413]).
(18)
Table 2. Estimated weights for DM1 based on the SWARA weighting method
Rankings
1
st
2nd
Compensation
Benefits
Multicriteria
50
3
rd
4th
5th
First-level Criteria
(Cj)
Conflict State (C2)
Natural
Environment (C1)
Health Care (C4)
Economic
Performance (C3)
Regulatory
Institution (C5)
Comparative
Importance
of Average,
sj
Coefficient,
kj = sj + 1
Re-calculated
Weights,
wj =
x j −1
kj
Scaled Weights,
qj =
wj
 w
m
j =1
1.0000
1.0000
0.3097
0.3571
1.3571
0.7368
0.2282
0.2143
1.2143
0.6068
0.1879
0.2857
1.2857
0.4720
0.1462
0.1429
1.1429
0.4130
0.1279
j
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
Table 3. Grey DM weights
Decision Makers (DMi) /
First Level Criteria (Cj)
C1
C2
C3
C4
C5
DM1
DM2
DM3
DM4
min wij
max wij
w j
0.2282
0.3097
0.1462
0.1879
0.1279
0.2258
0.3126
0.1834
0.1292
0.1490
0.2228
0.3038
0.1885
0.1253
0.1595
0.1969
0.2941
0.2406
0.1476
0.1208
0.1969
0.2941
0.1462
0.1253
0.1208
0.2282
0.3126
0.2406
0.1879
0.1595
[0.1744,0.2022]
[0.2605,0.2769]
[0.1295,0.2131]
[0.111, 0.1665]
[0.107, 0.1413]
1i  4
1i  4
4.2 Evaluation of Overseas Branches
The performance of all the alternatives to be evaluated for every second-level
criteria was obtained. The performances of these countries are given in Table 4.
Table 4. Performance of the alternatives for second-level indicators
Countries (i)/
Index Albania Algeria
Bangladesh
Second-level Criteria (Cj-k)
(n)
(1)
(2)
(3)
Clean Cities (C1-1)
1
18.2000
34.5000 58.6000
Environmental Performance Index (C1-2)
2
65.4600
57.1800 29.5600
Disaster Risk Index (C1-3)
3
9.5000
9.5000
1.6900
Global Terrorism Index (C2-1)
4
1.4870
3.9700
6.1810
Failed States Index (C2-2)
5
60.0793
75.7851 90.3128
Global Peace Index (C2-3)
6
1.8490
2.1820
2.0840
Consumer Price Index (C3-1)
7
115.0843
142.3842 161.1360
GDP per Capita (C3-2)
8
4537.8625 4123.3899 1516.5134
Inflation (C3-3)
9
1.2828
6.3977
5.5135
Sanitation and Hygiene (C4-1)
10
98.0000
87.0000 47.0000
Mortality From Environmental Pollution (C4-2)11
104.7000
40.3000 103.4000
Drinking Water (C4-3)
12
91.0000
93.0000 97.0000
Public Integrity Index (C5-1)
13
6.4800
4.9400
5.1700
Justices System (C5-2)
14
0.5078
0.4091
Reliability of Police Service (C5-3)
15
5.2000
4.7000
3.3000
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
Venezuela
(22)
16.8000
63.8900
36.2800
3.6320
86.2069
2.6420
2740.2740
254.9485
95.0000
28.9000
97.0000
1.9300
0.2863
1.8000
In this evaluation, there are three missing values: the Justices System (C5-2) in
Algeria, the Public Integrity Index (C5-1) in the UAE, and the GDP per Capita (C3-2)
of Venezuela. These missing values are ignored since second-level criteria are
conceptually correlated with respect to their first-level criteria. These missing
values would also not skew the results. 99.09% of the data were used for evaluation,
i.e., 327 out of 330 values. The evaluation, which is based on the steps in Section
3.3, is as follows:
Decision matrix X is constructed from Table 1 based on Equation (6):
x1 (15)   18.20 65.46
5.20  ,
 x1 (1) x1 (2)

 

x (1) x2 (2)
x2 (15)   34.50 57.18
4.70 
X = 2
=

 


 

x22 (15)   16.80 63.89
1.80 
 x22 (1) x22 (2)
Then, the normalized decision matrix is obtained using Equation (9)
.
0.2766  .
 0.2216 0.3400 0.7742


0.5356 0.4922 0.7742
0.3830 
X *=




1.0000 
 0.1946 0.3688 0.0000
Compensation
Benefits
Multicriteria
The grey data were computed using Equation (10) and are shown in Table 5.
51
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
Grey decision matrix  X is also constructed from Table 5.
 x1,5 
  x1,1  x1,2


 x2,1  x2,2
 x2,5 
X = 




 x22,5 
  x22,1  x22,2
  0.2216, 0.7742 0.1651, 0.492 
0.2766, 0.5768 


0.4922, 0.7742 0.4407, 0.6923
0.383, 0.5631 
=
.




0.4032, 0.8252
1, 1
  0, 0.3688

The weighted grey decision matrix was calculated using Equation (11). While
weights W were obtained using the SWARA weighting method for group
decision-making, as given in Equation (18), the weighted grey matrix is
  0.0386, 0.1565 0.0430, 0.1362
0.0296, 0.0815 


0.0858, 0.1565 0.1148, 0.1917
0.0410, 0.0796 
X  = 
,




0.1050, 0.2285
0.1070, 0.1413 
  0, 0.0746
and the reference country based on Equation (14) is
.
 X 0 = ( 0.1744,0.2022 ,  0.2429,0.2769 ,  0.1295,0.2131 ,  0.1110,0.1665 ,
0.1070,0.1413)
The series differences based on Equation (15) are presented in Table 5.
Table 5 Differences between reference country and evaluated countries
Criteria (Cj)
min  ij
C1
C2
C3
C4
C5
1 j  5
/Differences (  ij )
max  ij
1 j 5
1 j
0.1636
0.2339
0.2127
0.1632
0.1117
0.1117
0.2339
2 j
0.1164
0.1621
0.2114
0.1468
0.1003
0.1003
0.2114
 22 j
0.2022
0.1719
0.0836
0.1582
0.0343
0.0343
0.2022
min min  ij
1i  22 1 j 5
-
-
-
-
-
0.0278
-
max max  ij
-
-
-
-
-
-
0.2769
1i  22 1 j 5
The GRG using the distinguishing coefficient  =0.5 is
ri = ( r1 , r2 , r3 ,..., r22 ) ,
Compensation
Benefits
Multicriteria
52
(19)
= 0.5372, 0.5920, 0.6638, 0.5432, 0.5002, 0.5838, 0.5082, 0.5614, 0.5263,
0.5542, 0.5298, 0.5610, 0.7284, 0.7321, 0.5716, 0.5286, 0.5175, 0.5845, 0.5938,
0.5129, 0.5315, 0.6590.
As GRG increases, the less the favourable location is and, thus, the higher the
compensation. The location rankings of the branches, from most to least favourable
position, i.e., from the first to the 22 nd position, are: Canada, France, USA,
Romania, Italy, Poland, Malaysia, UAE, Albania, Brazil, Kazakhstan, Mexico,
Indonesia, Peru, Colombia, Russia, Algeria, Ukraine, Venezuela, Bangladesh,
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
Nigeria, Pakistan. The rankings and proposed scale for compensating expatriates
are shown in Figure 2.
Figure 2. Scaling and Rankings of Overseas Branches
It is interesting to observe that all the DMs ranked Conflict States (C2) as the
most important criterion, of which the allocated grey weight is [0.2605, 0.2769].
The least important criterion, with a grey weight of [0.107, 0.1413], was
Regulatory Institutions (C5). Canada was ranked in the first position, France was
ranked second, and the USA was ranked third. From the rankings, more allowance
should be allocated to expatriates who accept assignments in Nigeria and Pakistan,
the 21st and 22nd positions, respectively. As GRG increases, the more the locations
are unfavourable, thus the higher the compensation should be. The ratio of FSP
allowance to compensate expatriates is based on the GRG. For instance, if an
expatriate accepts an assignment in Albania, and they are paid ¥53,720
(Yuan—RMB), then the expatriate should be paid ¥65,900 if they accept an
assignment in Venezuela. Similarly, with the same ratio, the expatriate who accepts
an assignment in Canada should be paid just ¥50,020, whereas ¥73,210 should be
paid to them if they accepts an assignment in Pakistan.
The significant findings in the paper are expatriates accepting assignments in
developed countries should receive less FSP allowance than those giving
assignment in countries that are underdeveloped, with harsh and risky work
environments. Although the FSP allowance does not fully explain the high turnover
rate in the Nigeria and Pakistan branches, the FSP allowance ratio shows that more
justice can be done to compensate expatriates in these branches by increasing their
FSP allowance by a good proportion. The initial cost-saving of paying expatriates
less may seem like a good strategy but, in the long run, expatriates quitting can be a
huge loss to a company[102]. With the method presented in this paper, the DMs felt
justified in scaling FSP allowance of expatriates instead of individually responding Compensation
to every request for a pay raise. In addition, the result took the company a step
Benefits
closer to meeting the expectation of expatriates[103].
Multicriteria
53
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
5.Conclusion
In general terms, we understand compensation to be the payment that
employees receive in exchange for their work and contribution to the organisation.
FSP allowance is nothing more than a balancing mechanism where expatriates are
compensated for their effort with a lump sum. In this sense, “fair compensation”
would be one that achieves a reasonable balance between what the expatriate gives
and what they receive. From the employees’ point of view, their allowance
becomes one of the main factors that they take into account when accepting an
assignment overseas. The goal of these C and B policies is to ensure that expatriate
workers, in any of their modalities, as well as their families, are supported by
common, homogeneous, and competitive policies and practices. FSP allowance
could improve their purchasing power, security, and comfort in the country of
destination, as well as the professional attractiveness of the international project
assigned to it.
Compensation
Benefits
Multicriteria
54
The contributions of this study are as follows: Firstly, a simple
hierarchical diagram for FSP allowance for overseas branches was presented.
Secondly, a hybrid MCDM method that combines the SWARA and GRA
techniques with GN was also presented. This hybrid method is well-suited for
group decision-making in an uncertain decision-making environment. Thirdly,
part of the solution to the problem of employee turnover in a company is
presented, which is scaling FSP allowance for expatriates in overseas branches.
Now, it is important for employees to understand how they are paid in the
global field. The more that an employee understands how their bonuses and
merit increments are calculated, especially if they are expatriates, the easier it is
for HR managers to answer any questions, concerns, or complaints they have in
this area. Furthermore, this paper can help companies develop a transparent
system for compensating expatriates that may be deemed fair by the employee,
encouraging staff to take up assignments at very challenging and less
favourable environments.
This research has some limitations. It was difficult for us to obtain
primary data from all overseas branches based on the evaluated criteria, so
secondary data was used. The result of this research is dependent on the
accuracy of the data provided by these sources. Moreover, it may be difficult to
truly represent real conditions in the local environment of an overseas branch.
Furthermore, there are many factors that can lead to expatriate turnover, such
as job satisfaction and organization commitment, which the current study did
not cover[102].
Further research can be done to provide different scaling factors for male
and female staff since deal-breakers for women expatriates to accept a foreign
assignment may be different from those of male expatriates[5]. Another area of
research can be measuring how long-service allowance can delay staff from
retirement[104]. Thereby, the company can benefit from the cumulative wealth
of the ageing employees. Long-service allowance could result in a win-win
situation because more of the company’s goals would be met. Finally, this
problem may be solved as a mathematical programming model in which the
scaling FSP would be addressed as a set-partitioning formulation with capacity
(budget constraints)[105].
Moses Olabhele Esangbedo et al/ The Journal of Grey System 2020 (32)
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