The 15th QiR 2017 revised paper 591 MarkusHartono
How Kansei Engineering, Kano and QFD improve logistics services
Markus Hartono1, a, Amelia Santoso1, Dina Natalia Prayogo1
1
Department of Industrial Engineering, University of Surabaya, Indonesia
a
markus@staff.ubaya.ac.id
Keywords: Kansei Engineering, Kano, QFD, logistics, services
Abstract. In the period of 2004 – 2014, there was a significant growth of employment in logistics
sector in Indonesia. It shows that there is an opportunity to probe problems and achieve
improvements in logistics sectors. Inherently, it shows a global trend, which is a rapid need for
outsourcing the supporting logistics activities. It makes the logistics service provider (known as thirdparty logistics) has a beneficial portion in the international and domestic supply chain. With regard to
the very tight competition, the logistics services should be able to deliver both cognitive and affective
customer satisfaction. In the operational point of view, customer satisfaction and lifetime values
offered are the critical attributes to the success of logistic services. Mostly, studies in logistic services
have been focusing on the service gaps, which is more on cognitive process. Actually, many
researches have been conducted in evaluating the logistics service quality using SERVQUAL and
Kano model. However, it is relatively insufficient. Hence, a deep understanding of customer affective
need (known as Kansei, in Japanese) is highly required, as a competitive advantage to explore and
model more comprehensive customer experiences due to perceived certain logistics services. This
paper proposes a model of Kansei Engineering, Kano and QFD, which is hoped to generate more
innovative ideas for improvements. Surely, those which are critical and sensitive to the customer
emotional satisfaction will be of interest. Moreover, it leads to the customer delight, which is beyond
satisfaction. A case study in the supporting logistics services has been chosen to validate the
proposed model. A survey through face-to-face questionnaire involved 157 customers has been done.
Afterwards, the model has been validated, and through House of Quality (HoQ), it has been proposed
some innovative improvement ideas. They include the use of apps for order confirmation and
cancellation, the integration of Google Maps to the ordering system, pre-order booking, and the
feature of bilingual in the transaction menu. Thus, in practical implication and point of view, this
study is hoped to provide guideline to the manager of logistics services’ company in capturing,
measuring and analyzing the customer emotional need (Kansei), with respect to the service attributes
which are highly significant to those Kansei.
Introduction
Perceived service quality will affect satisfaction that leads to customer loyalty. In other words,
customer loyalty will be affected by perceived quality indirectly. The service quality can be
considered as a composite of multiple service attributes which are structured into tangible and
intangible/subjective attributes [1]. The assessment of service quality can be judged by the gap
between perceived quality and the customer expectation, which is known as SERVQUAL [2]. The
application of SERVQUAL has been extensively broad, since 1990s. In particular service domain,
the common dimensions of SERVQUAL discussed are tangibles, reliability, responsiveness,
assurance and empathy.
One of the emerging services is that logistics services, especially third-party logistics (3PL).
According to Chen et al. [3], this type of logistics services can add a great value to customers and
companies. There are three major services offered, such as package pick-up, tracking, and delivery
services. Moreover, these services may play a critical part in leveraging the effectiveness and
efficiency of the physical distribution and online transactions of goods, even of services [4].
Service experiences will be started by human and ended by the human as well. It starts with
customer needs, and will be closed by customer satisfaction and loyalty. The exploration of research
on human involvement n service encounters is relatively less [5]. Both servicescape and interaction
between customers and employees will build service satisfaction. In other words, it is said that
processes, activities and interactions are more dominant than things [6; 7]. Regarding human-based
interaction, service experiences will produce both cognitive and affective satisfaction (see [8] and
[9]).
In obtaining a competitive advantage with respect to customer behavior, services should put more
efforts on integrating human factors into service design [10]. The scopes of human factors (known as
Ergonomics) cover physical and psychological human behaviors, environments, products and
services [3]. Inherently, the concept of Ergonomics has been extensively extended from physical
product to service designs. Hence, the service provider should understand how customers expect and
perceive the services [11].
Products and services are deemed to be successful when they can produce happiness to the
customers or users [12]. Moreover, emotional satisfaction is hoped to be put beyond the usability and
functionality [13; 9]. In dealing, capturing and modeling customer emotional needs into service
design and development, Kansei Engineering [KE] is proposed [14; 15; 16]. Since 1970s, it is the
ultimate ergonomic-based product design development which puts emotions into its core concept,
and later quantifies them into design specifications [14].
According to Chen et al. [3], KE has been applied to the design of physical products such as
architectural interiors and exteriors, consumer goods, mobile phones, and even sport shoes. Mostly,
the designers use Kansei words as the representative of emotional needs which have been translated
into product elements. The use of KE in services is deemed to be limited. Its application into services
may cover delivery and installation of a washing machine [17], internet services [18], hotel services
[16], restaurant services [19], and logistics services [3]. One of the superiorities of KE is that its
ability to show the interactive relationship between design characteristics and emotional responses,
hereinafter it establishes a quantitative framework.
Regarding the logistics service as one of the emerging services nowadays, KE has been applied, as
it has been conducted by Chen et al. [3]. Recent research of KE in logistics services has been done by
exploring the quantitative relationship between feelings (using Kansei words) and design elements of
home delivery services. It shows that what the most important design elements connected to the
critical feelings, which refer to the improvement ideas. However, due to efficiency concern, this
study by Chen et al. [3] can be strengthen and extended by incorporating potential quality tools, such
as Kano model and QFD. According to Hartono [20], the use of Kano model and QFD in Kansei
methodology research may provide a formal methodology which accounts for customer emotional
needs in service design. Hence, this study of KE integrated with Kano model and QFD in logistics
services is proposed. Kano will help a screening process to identify which service attributes are
categorized as one-dimensional (O) and attractive (A) which are critical to Kansei, whereas QFD will
finalize the weighted prioritized service attributes to improve [see 16].
Thus, the objective of this study is to develop a conceptual framework of KE, Kano and QFD
applied to logistics services, and to conduct an empirical study on IT-based logistics services to test
the applicability of the proposed model. The details of the superiority of current integrative
framework compared to the individual method are summarized in the table below:
Table 1. The rational contrast between individual method and the proposed integrative framework
Individual method
Kansei Engineering is used as a function between Kansei
and service experience, or in other words, it is a
methodology to translate customer emotional feelings into
service characteristics. However, it lacks of the knowledge
of which service attributes are important and urgent to be
taken care of.
Kano model is to categorize service performance into 3
main categories, namely, attractive [A], one-dimensional
[O], and basic/must-be [M]. However, it lacks of the
knowledge of which service attributes are sensitive to
particular Kansei.
Proposed integrative framework
Hence, to overcome all the defined deficiencies, the
integrative framework of Kansei Engineering, Kano and
QFD has been proposed. It is to link the sensitive or urgent
customer emotional needs (known as Kansei) with service
attributes experience, and to prioritize which service
attributes are to be improved taking into account their
impact on Kansei.
QFD is to translate customer needs into product or service
elements/characteristics. However, it lacks of the specific
customer needs (for instance, customer emotional
needs/Kansei) and the weighting scale formulation.
With regard to the details of the proposed approach shown in Table 1, the expected contribution of
this current study as contrasted to the previous research on Kansei is as follows. The current study
will complete the broader application of Kansei Engineering in different service setting, which is
logistics services. The use of QFD accompanied by Pareto diagram is expected to explore and
consider more practical solutions based on the current best practice improvements.
Following the Introduction section, this paper is organized as follows. There will be literature
review, in which there is a review of recent research on KE applied in services. Afterwards, research
methodology and framework development are provided. A case study followed by its findings will be
discussed in result and discussion section. Then, it will be wrapped up as a conclusion and further
recommendation.
Brief Literature Review
Kansei Engineering in services. Referring to Nagamachi [14] and Nagamachi & Lokman [21],
research of KE is ranged from physical product to customer service (which is known as Kansei
quality management). Essentially, the core benefit gained is the same, which to start and end with
customer emotional needs. More specifically, research of KE in services has been introduced and
applied into hotel [16; 9], restaurant [19], and even extended into interior design [22]. The same
format of KE model is that Kansei has been defined as the function of perceived services. By taking
the current issue of sustainability, KE has been extended to tackle today’s problems. The most recent
research on KE which incorporates more efficient approach has been addressed, which is known as
an extended model of KE, Kano and TRIZ to solve some potential contradictions among solutions
[19]. It, then, has been extended to cover sustainability issues that are covering environmental,
economical, and social elements. In term of research gap identified, a short summary of KE research
on services in the last 6 years, provided in a matrix of author(s) and main
concern(s)/tool(s)/method(s) is shown in Table 2.
Table 2. Recent research on Kansei Engineering applied in services
Author(s)
Llinares & Page, 2011 [22]
Hartono & Tan, 2011 [16]
Hartono, 2012 [20]
Rasamoelina et al., 2013 [23]
Hartono et al., 2013 [24]
Hartono, 2014 [25]
Hartono & Raharjo, 2015 [9]
Chen et al., 2015 [3]
Hartono, 2016a [19]
Hartono, 2016b [26]
Current research
remark: √ = related
General KE
√
√
√
√
√
√
√
√
√
√
√
SERVQUAL
√
√
√
√
√
√
√
√
Concerns/Tools/Methods
Kano TRIZ Cultures
√
√
√
√
√
√
√
√
√
√
√
√
√
√
Sustainability
Logistics
√
√
√
√
According to what is provided in Table 1, this current research shows a position in which KE may
contribute to the field of logistics services (i.e., third party logistics – 3PL) using general KE
methodology integrated with SERVQUAL and Kano model. The choice of logistics field is hoped to
generate more practical contribution to today’s trend in services.
Kano model in services. According to Hartono and Tan [16], Kano model is deemed to
strengthen the KE methodology by providing a guideline of how customers rate their satisfaction due
to perceived services. Those what have been rated as one-dimensional/linear satisfaction or
attractive/delighter [27] are quite related to Kansei-based experience [16]. One-dimensional
satisfaction provides a linear relationship between product characteristics fulfillment and satisfaction
level, whereas attractive satisfaction will be more on latent need which is unspoken need. Once it is
fulfilled, it will generate unpredictable satisfaction. Otherwise, it will give normal satisfaction. More
specifically, it is beyond usability and satisfaction. It is hoped that the delighted customers will have
an emotional bonding with a particular service provider.
SERVQUAL model and logistics services. In this study, the service quality for logistics services
is modeled and measured by SERVQUAL (see [2] for details), which consists of 5 dimensions (i.e.,
tangibles, reliability, responsiveness, empathy, and assurance). SERVQUAL scales will serve as the
measurement instrument of perceived and expected services. Basically, logistics service quality is
that the overall and comprehensive activities ranging from order receipt to delivery to the customers.
Related to logistics services, one of the most interesting types for research is that home delivery
service, as it has been done by Chen et al.[3]. Another interesting type, which is becoming a global
trend, is that logistic service provider (known as third party logistics – 3PL). Third party logistics is
deemed to be a critical position in the supply chain for international and domestic trading. According
to customer point of view, Franceschini & Rafele [28] stated that the logistics services can be
measured through lead-time, regularity, reliability, flexibility, preciseness, harmfulness and
productivity. In this current research, it is used 3PL which can be scaled and customized to customer
needs such as the demand and delivery service requirements. It may cover products/goods, humans,
and several forms of services. In other words, this 3PL may go beyond logistics which include valueadded activities.
Framework Development and Research Methodology
Framework development. Based on the research background and formulized state of the art of
the KE research in services, a research framework of KE incorporating Kano and QFD is developed
(as shown in Fig. 1). It starts with the problems faced by particular logistics services company, and
then, spans the Kansei (as the response variable) and perceived service attribute performance
(functioned as the predictor variable). Concurrently, Kano categorization process is done to filter the
one-dimensional and attractive performance (O and A category) which are sensitive to the Kansei. By
generating a linear model continued with the calculation of satisfaction score (see [29]), prioritized
improvement for particular service attribute(s) is defined. It is, then, wrapped up with how to
generate design specification(s) through House of Quality.
Research methodology. By adopting methodology proposed by Hartono [19; 26], this study
utilizes survey through face-to-face questionnaire, convenience sampling plan, and involving a
specific target of respondents for particular logistics services. Those who were experienced logistics
services from XYZ company at least twice within a year have been selected as the potential subjects.
Items in the questionnaire were made based on the literature review, interview with actual users, and
personal observation.
Fig. 1. The Framework of KE, Kano and QFD for logistics services
Case study on IT-based application logistics services
A case study on IT-based application supporting logistics services in Surabaya has been taken. It
was called XYZ. A hundred fifty seven actual respondents were involved in the study. The subjects
were those experiencing services of XYZ with a period of August – October 2016. They were 54%
female and 46% male, with a majority of aged 16 – 25 years (55%), followed by 26 – 35 years
(27%), 36 – 45 years (14%), and above 45 years (at about 4%). Mostly they were college students
(39%), and followed by professionals (31%), entrepreneurs (17%), and the rest were housewives.
Referring to discrepancy between perceived and expected logistics services, service gap has been
calculated in each of logistics service attributes (as shown in Table 3). Afterwards, in order to
confirm that the gap was significant, the t-test for comparing two sample means has been done. The
results of t-test are provided in Table 3 as well. It shows that, in all service attributes, H0 was
rejected. It means that, to all logistics service attributes, the perceived service was less than the
expected one. The customers felt that what they have received was not met with what they have
expected.
Table 3. The statistical test for logistics service gap
No
1
2
3
4
5
6
7
8
Logistics service attributes
Vehicle type
Cleanliness of vehicle
Driver performance
Completeness of driver’s attributes
Driver rating score
Web-based application interface
Cleanliness of helmet for customer
Provision of mask
Gap*
Tangibles (T)
-0.56
-0.86
-0.65
-0.76
-0.37
-0.39
-1.27
-1.25
tvalue
pvalue
-7.733
-11.852
-8.664
-9.390
-4.984
-4.804
-15.092
-14.352
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
<
<
<
<
<
<
<
<
α
Remark
0.05
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
9
10
Food receipt
Appearance of foods ordered
-0.66
-8.399
-0.90 -10.524
Empathy (E)
11 Provision of apology once any mistakes
-0.71
-7.853
12 Confirmation for any unavailability of orders
-0.74
-9.432
13 Confirmation for any cancellations
-0.97 -12.590
Responsiveness (R)
14 Friendliness of driver
-0.66
-8.758
15 Politeness of driver
-0.82 -11.067
16 Promptness of delivery
-1.01 -11.221
17 Confirmation for any orders made
-0.47
-5.543
18 Knowledge of driver for any interesting places
-0.99 -12.256
Reliability (Re)
19 Accuracy of payment
-0.78
-9.349
20 Accuracy of driver identity
-0.79
-9.831
21 Accuracy of promotions
-0.55
-7.361
22 Accuracy of orders
-0.81 -10.925
23 Safety
-0.87 -13.978
Assurance (A)
24 Driver traceability
-0.81 -10.000
25 Warranty for orders
-0.67
-7.578
26 Privacy for customer
-0.76
-9.630
*the difference between perceived and expected service
0.000
0.000
<
<
0.000
0.000
0.000
<
<
<
0.000
0.000
0.000
0.000
0.000
<
<
<
<
<
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
H0 rejected
H0 rejected
0.05
H0 rejected
H0 rejected
H0 rejected
0.05
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
<
<
<
<
<
0.05
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
<
<
<
0.05
H0 rejected
H0 rejected
H0 rejected
Perceived logistics services influenced particular Kansei (i.e., emotional satisfaction). In this
study, there were 10 Kansei identified, formulized and measured, i.e., helped (mean = 4.09), trusted
(mean = 3.93), secured (mean = 3.91), comfortable (mean = 3.85), innovative (mean = 3.83), friendly
(mean = 3.79), precise (mean = 3.70), professional (mean = 3.57), prompt (mean = 3.44) and cheap
(mean = 3.32). The distribution of perceived Kansei scores is shown in Fig. 2.It shows that Kansei
“helped” has the highest perceived score; it means that in general, the customers felt “helped” once
they have received logistics services from company XYZ.
4,5
4
3,5
4,0955 3,9299
3,9108 3,8535 3,828 3,7898
3,7006 3,5732
3,4395 3,3248
3
2,5
2
1,5
1
0,5
0
Fig. 2. Distribution of perceived Kansei in logistics services
Afterwards, through Kano categorization process, with respect to attractive [A] and onedimensional [O] categories, those logistics service attributes belong to were identified. They were
then connected to significant Kansei through linear model test, and calculated their satisfaction
scores. According to Hartono & Tan [16], the importance weight was determined by incorporating
the value of satisfaction score, Kano weight [see [29]], and Kansei score. The higher the importance
weight, the more important the service attribute is. The result is shown in Table 4.
No
Code
1
T2
2
T6
3
T7
4
T10
5
E11
6
E13
Table 4. The importance weight of logistics service attributes
Logistics service
|Satisfaction Kano
Kansei score
attributes
score|*
weight
Cleanliness of vehicle
3.47
A 4
Secured
3.91
Friendly
3.79
Web-based application
1.58
A 4
Innovative
3.83
interface
Helped
4.09
Professional 3.57
Innovative
3.83
Cleanliness of helmet
5.73
O 2
for customer
Cheap
3.32
Precise
3.70
Appearance of foods
3.84
A 4
Helped
4.09
ordered
Provision of apology
3.06
A 4 Comfortable 3.85
once any mistakes
Confirmation for any
cancellations
4.16
O
Helped
4.09
Comfortable
3.85
Trusted
Friendly
Prompt
3.93
3.79
3.44
Importance
weight**
54.27
2
74.00
165.25
62.82
47.12
66.07
7
8
R14
R15
Friendliness of driver
Politeness of driver
2.95
3.69
O
O
2
2
9
R16
Promptness of delivery
4.52
A
4
10
R18
4.24
A
4
Precise
3.70
62.75
11
A24
Knowledge of driver for
any interesting places
Driver traceability
3.44
O
2
A25
Warranty for orders
2.74
A
4
4.09
3.93
3.91
3.85
28.14
12
Helped
Trusted
Secured
Comfortable
*|satisfaction score| = (perceived – expected) x importance level of service
**importance weight = |satisfaction score| x Kano weight x Kansei score
5.9
29.00
130.71
128.12
120,00%
180
160
100,00%
140
80,00%
120
100
60,00%
80
Importance of What
% Kumulatif
40,00%
60
40
20,00%
20
0,00%
R14
A24
R15
E11
T2
R18
T10
E13
T6
A25
R16
T7
0
Fig. 3. The Pareto chart of logistics service attributes based on importance weight
With regard to the Pareto chart as shown in Figure 3, there were 7 logistics service attributes
deemed to be critical, i.e., T7 (cleanliness of helmet for customer), R16 (promptness of delivery),
A25 (warranty for orders), T6 (web-based application interface), E13 (confirmation for any
cancellations), T10 (appearance of foods ordered) and R18 (knowledge of driver for any interesting
places). Using a House of Quality (HoQ), some related design specifications (known as metrics) have
been formulized, as shown in Figure 4. It shows that the most critical improvement idea was that the
provision of modular system for helmet (inside and outside part) for the customer.
Discussion
This study was actually done as the extension of previous research on KE, Kano and QFD applied
in services (see [24]). By taking a case on logistics services, this study is hoped to contribute on the
efficiency of logistics performance. The field of logistics services becomes a potential niche to
explore. There is a huge market for logistics in Indonesia in year 2004 – 2014, and it becomes larger
and larger due to the growth of infrastructures and economic development. Moreover, with respect to
customer point of view, third-party logistics (3PL) was chosen since this kind of services can add a
great value to customers and companies [3].
With regard to the potential development of emotional-based service quality tools and potential
needs for 3PL services, this study has been conducted. It proposed a framework of KE, Kano and
QFD applied to one of popular IT-based supporting logistics services in Surabaya. Basically, this
company provided services on logistics either for foods, documents or passengers.
According to the research findings, it has been shown that the attribute “cleanliness of helmet for
company” was the most important one, which had significant correlation with Kansei word
“professional, innovative, cheap, and precise”. Given a very limited time, effort, budget or other
resources, the company should focus more on the cleanliness of helmet and its supporting facilities in
order to gain more customer emotional satisfaction. Though, the Kansei “helped” was of to be the
highest rated emotion experienced by the customers. It was influenced by the performance of
attribute “web-based application interface”, “appearance of food ordered”, “confirmation for any
cancellations”, and “driver traceability”. In other words, in general, the customers felt helped once
they were served by the XYZ company.
It is, also, suggested that the modular system for helmet (inside & outside part) was proposed. It
was deemed as the most prominent improvement, and then followed by the provision of application
software to give comment and rating anonymously, and also the use of application software for
confirmation and cancellation.
Conclusion, limitation and further recommendation
Conclusion. This study promotes the major role of human factors, especially Kansei, in
influencing the efficiency and effectiveness of logistics service design and development. The
integrated model of KE, Kano and QFD provides the understanding of what should be considered
and executed by the service manager or provider in improving the services offered, yet still focusing
on the prioritized solutions, given very limited resources. In this study, the improvement on helmet
system, and the provision of application software for submitting comment and rating, and doing
confirmation/cancellation were rated as high priorities.
Limitation and further recommendation. This study is of limited by a relative small sample
size, and a specific IT-based logistics services. Since the proposed model is meant to be a general
model for the improvement of logistics services, more empirical studies are required. Moreover, the
exploration on another part of logistics services is needed, not only 3PL which focuses on the end
customers, but also on more upstream entities.
Importance weight
165.25
R16
130.71
A25
128.12
T6
74
E13
66.07
T10
62.82
R18
62.75
The integration of Google Maps to the ordering system
The use of multifunction bag
The use of apps for order confirmation and cancellation
The thematic design of apps (i.e., Christmas, New Year)
The app for anonymous rating and comment
The app used by driver to order directly to vendor
The modular system for helmet (inside & outside part)
WHAT(s)
Attribute
T7
HOW(s)
9
9
3
9
3
9
3
3
9
9
9
Total weight
1487.25
1176.39
1351.29
666
1371.12
565.38
786.75
Percentage
20.09%
15.89%
18.25%
8.99%
18.52%
7.64%
10.63%
Fig. 4. The simple form of HoQ for IT-based logistics services improvement
Acknowledgement
This study on the model development and its application on logistics services were fully supported by
the research grant under the scheme of applied product with a contract number of 22/SP-Lit/LPPM01/Dikti/FT/V/2017, endorsed by the Directorate of Higher Education, the Ministry of Research,
Technology, and Higher Education, Republic of Indonesia. Also, this was partially supported by the
Department of Industrial Engineering, University of Surabaya, Indonesia.
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development. Managing Service Quality, Vol. 11 (2001), pp. 418–430.
Markus Hartono1, a, Amelia Santoso1, Dina Natalia Prayogo1
1
Department of Industrial Engineering, University of Surabaya, Indonesia
a
markus@staff.ubaya.ac.id
Keywords: Kansei Engineering, Kano, QFD, logistics, services
Abstract. In the period of 2004 – 2014, there was a significant growth of employment in logistics
sector in Indonesia. It shows that there is an opportunity to probe problems and achieve
improvements in logistics sectors. Inherently, it shows a global trend, which is a rapid need for
outsourcing the supporting logistics activities. It makes the logistics service provider (known as thirdparty logistics) has a beneficial portion in the international and domestic supply chain. With regard to
the very tight competition, the logistics services should be able to deliver both cognitive and affective
customer satisfaction. In the operational point of view, customer satisfaction and lifetime values
offered are the critical attributes to the success of logistic services. Mostly, studies in logistic services
have been focusing on the service gaps, which is more on cognitive process. Actually, many
researches have been conducted in evaluating the logistics service quality using SERVQUAL and
Kano model. However, it is relatively insufficient. Hence, a deep understanding of customer affective
need (known as Kansei, in Japanese) is highly required, as a competitive advantage to explore and
model more comprehensive customer experiences due to perceived certain logistics services. This
paper proposes a model of Kansei Engineering, Kano and QFD, which is hoped to generate more
innovative ideas for improvements. Surely, those which are critical and sensitive to the customer
emotional satisfaction will be of interest. Moreover, it leads to the customer delight, which is beyond
satisfaction. A case study in the supporting logistics services has been chosen to validate the
proposed model. A survey through face-to-face questionnaire involved 157 customers has been done.
Afterwards, the model has been validated, and through House of Quality (HoQ), it has been proposed
some innovative improvement ideas. They include the use of apps for order confirmation and
cancellation, the integration of Google Maps to the ordering system, pre-order booking, and the
feature of bilingual in the transaction menu. Thus, in practical implication and point of view, this
study is hoped to provide guideline to the manager of logistics services’ company in capturing,
measuring and analyzing the customer emotional need (Kansei), with respect to the service attributes
which are highly significant to those Kansei.
Introduction
Perceived service quality will affect satisfaction that leads to customer loyalty. In other words,
customer loyalty will be affected by perceived quality indirectly. The service quality can be
considered as a composite of multiple service attributes which are structured into tangible and
intangible/subjective attributes [1]. The assessment of service quality can be judged by the gap
between perceived quality and the customer expectation, which is known as SERVQUAL [2]. The
application of SERVQUAL has been extensively broad, since 1990s. In particular service domain,
the common dimensions of SERVQUAL discussed are tangibles, reliability, responsiveness,
assurance and empathy.
One of the emerging services is that logistics services, especially third-party logistics (3PL).
According to Chen et al. [3], this type of logistics services can add a great value to customers and
companies. There are three major services offered, such as package pick-up, tracking, and delivery
services. Moreover, these services may play a critical part in leveraging the effectiveness and
efficiency of the physical distribution and online transactions of goods, even of services [4].
Service experiences will be started by human and ended by the human as well. It starts with
customer needs, and will be closed by customer satisfaction and loyalty. The exploration of research
on human involvement n service encounters is relatively less [5]. Both servicescape and interaction
between customers and employees will build service satisfaction. In other words, it is said that
processes, activities and interactions are more dominant than things [6; 7]. Regarding human-based
interaction, service experiences will produce both cognitive and affective satisfaction (see [8] and
[9]).
In obtaining a competitive advantage with respect to customer behavior, services should put more
efforts on integrating human factors into service design [10]. The scopes of human factors (known as
Ergonomics) cover physical and psychological human behaviors, environments, products and
services [3]. Inherently, the concept of Ergonomics has been extensively extended from physical
product to service designs. Hence, the service provider should understand how customers expect and
perceive the services [11].
Products and services are deemed to be successful when they can produce happiness to the
customers or users [12]. Moreover, emotional satisfaction is hoped to be put beyond the usability and
functionality [13; 9]. In dealing, capturing and modeling customer emotional needs into service
design and development, Kansei Engineering [KE] is proposed [14; 15; 16]. Since 1970s, it is the
ultimate ergonomic-based product design development which puts emotions into its core concept,
and later quantifies them into design specifications [14].
According to Chen et al. [3], KE has been applied to the design of physical products such as
architectural interiors and exteriors, consumer goods, mobile phones, and even sport shoes. Mostly,
the designers use Kansei words as the representative of emotional needs which have been translated
into product elements. The use of KE in services is deemed to be limited. Its application into services
may cover delivery and installation of a washing machine [17], internet services [18], hotel services
[16], restaurant services [19], and logistics services [3]. One of the superiorities of KE is that its
ability to show the interactive relationship between design characteristics and emotional responses,
hereinafter it establishes a quantitative framework.
Regarding the logistics service as one of the emerging services nowadays, KE has been applied, as
it has been conducted by Chen et al. [3]. Recent research of KE in logistics services has been done by
exploring the quantitative relationship between feelings (using Kansei words) and design elements of
home delivery services. It shows that what the most important design elements connected to the
critical feelings, which refer to the improvement ideas. However, due to efficiency concern, this
study by Chen et al. [3] can be strengthen and extended by incorporating potential quality tools, such
as Kano model and QFD. According to Hartono [20], the use of Kano model and QFD in Kansei
methodology research may provide a formal methodology which accounts for customer emotional
needs in service design. Hence, this study of KE integrated with Kano model and QFD in logistics
services is proposed. Kano will help a screening process to identify which service attributes are
categorized as one-dimensional (O) and attractive (A) which are critical to Kansei, whereas QFD will
finalize the weighted prioritized service attributes to improve [see 16].
Thus, the objective of this study is to develop a conceptual framework of KE, Kano and QFD
applied to logistics services, and to conduct an empirical study on IT-based logistics services to test
the applicability of the proposed model. The details of the superiority of current integrative
framework compared to the individual method are summarized in the table below:
Table 1. The rational contrast between individual method and the proposed integrative framework
Individual method
Kansei Engineering is used as a function between Kansei
and service experience, or in other words, it is a
methodology to translate customer emotional feelings into
service characteristics. However, it lacks of the knowledge
of which service attributes are important and urgent to be
taken care of.
Kano model is to categorize service performance into 3
main categories, namely, attractive [A], one-dimensional
[O], and basic/must-be [M]. However, it lacks of the
knowledge of which service attributes are sensitive to
particular Kansei.
Proposed integrative framework
Hence, to overcome all the defined deficiencies, the
integrative framework of Kansei Engineering, Kano and
QFD has been proposed. It is to link the sensitive or urgent
customer emotional needs (known as Kansei) with service
attributes experience, and to prioritize which service
attributes are to be improved taking into account their
impact on Kansei.
QFD is to translate customer needs into product or service
elements/characteristics. However, it lacks of the specific
customer needs (for instance, customer emotional
needs/Kansei) and the weighting scale formulation.
With regard to the details of the proposed approach shown in Table 1, the expected contribution of
this current study as contrasted to the previous research on Kansei is as follows. The current study
will complete the broader application of Kansei Engineering in different service setting, which is
logistics services. The use of QFD accompanied by Pareto diagram is expected to explore and
consider more practical solutions based on the current best practice improvements.
Following the Introduction section, this paper is organized as follows. There will be literature
review, in which there is a review of recent research on KE applied in services. Afterwards, research
methodology and framework development are provided. A case study followed by its findings will be
discussed in result and discussion section. Then, it will be wrapped up as a conclusion and further
recommendation.
Brief Literature Review
Kansei Engineering in services. Referring to Nagamachi [14] and Nagamachi & Lokman [21],
research of KE is ranged from physical product to customer service (which is known as Kansei
quality management). Essentially, the core benefit gained is the same, which to start and end with
customer emotional needs. More specifically, research of KE in services has been introduced and
applied into hotel [16; 9], restaurant [19], and even extended into interior design [22]. The same
format of KE model is that Kansei has been defined as the function of perceived services. By taking
the current issue of sustainability, KE has been extended to tackle today’s problems. The most recent
research on KE which incorporates more efficient approach has been addressed, which is known as
an extended model of KE, Kano and TRIZ to solve some potential contradictions among solutions
[19]. It, then, has been extended to cover sustainability issues that are covering environmental,
economical, and social elements. In term of research gap identified, a short summary of KE research
on services in the last 6 years, provided in a matrix of author(s) and main
concern(s)/tool(s)/method(s) is shown in Table 2.
Table 2. Recent research on Kansei Engineering applied in services
Author(s)
Llinares & Page, 2011 [22]
Hartono & Tan, 2011 [16]
Hartono, 2012 [20]
Rasamoelina et al., 2013 [23]
Hartono et al., 2013 [24]
Hartono, 2014 [25]
Hartono & Raharjo, 2015 [9]
Chen et al., 2015 [3]
Hartono, 2016a [19]
Hartono, 2016b [26]
Current research
remark: √ = related
General KE
√
√
√
√
√
√
√
√
√
√
√
SERVQUAL
√
√
√
√
√
√
√
√
Concerns/Tools/Methods
Kano TRIZ Cultures
√
√
√
√
√
√
√
√
√
√
√
√
√
√
Sustainability
Logistics
√
√
√
√
According to what is provided in Table 1, this current research shows a position in which KE may
contribute to the field of logistics services (i.e., third party logistics – 3PL) using general KE
methodology integrated with SERVQUAL and Kano model. The choice of logistics field is hoped to
generate more practical contribution to today’s trend in services.
Kano model in services. According to Hartono and Tan [16], Kano model is deemed to
strengthen the KE methodology by providing a guideline of how customers rate their satisfaction due
to perceived services. Those what have been rated as one-dimensional/linear satisfaction or
attractive/delighter [27] are quite related to Kansei-based experience [16]. One-dimensional
satisfaction provides a linear relationship between product characteristics fulfillment and satisfaction
level, whereas attractive satisfaction will be more on latent need which is unspoken need. Once it is
fulfilled, it will generate unpredictable satisfaction. Otherwise, it will give normal satisfaction. More
specifically, it is beyond usability and satisfaction. It is hoped that the delighted customers will have
an emotional bonding with a particular service provider.
SERVQUAL model and logistics services. In this study, the service quality for logistics services
is modeled and measured by SERVQUAL (see [2] for details), which consists of 5 dimensions (i.e.,
tangibles, reliability, responsiveness, empathy, and assurance). SERVQUAL scales will serve as the
measurement instrument of perceived and expected services. Basically, logistics service quality is
that the overall and comprehensive activities ranging from order receipt to delivery to the customers.
Related to logistics services, one of the most interesting types for research is that home delivery
service, as it has been done by Chen et al.[3]. Another interesting type, which is becoming a global
trend, is that logistic service provider (known as third party logistics – 3PL). Third party logistics is
deemed to be a critical position in the supply chain for international and domestic trading. According
to customer point of view, Franceschini & Rafele [28] stated that the logistics services can be
measured through lead-time, regularity, reliability, flexibility, preciseness, harmfulness and
productivity. In this current research, it is used 3PL which can be scaled and customized to customer
needs such as the demand and delivery service requirements. It may cover products/goods, humans,
and several forms of services. In other words, this 3PL may go beyond logistics which include valueadded activities.
Framework Development and Research Methodology
Framework development. Based on the research background and formulized state of the art of
the KE research in services, a research framework of KE incorporating Kano and QFD is developed
(as shown in Fig. 1). It starts with the problems faced by particular logistics services company, and
then, spans the Kansei (as the response variable) and perceived service attribute performance
(functioned as the predictor variable). Concurrently, Kano categorization process is done to filter the
one-dimensional and attractive performance (O and A category) which are sensitive to the Kansei. By
generating a linear model continued with the calculation of satisfaction score (see [29]), prioritized
improvement for particular service attribute(s) is defined. It is, then, wrapped up with how to
generate design specification(s) through House of Quality.
Research methodology. By adopting methodology proposed by Hartono [19; 26], this study
utilizes survey through face-to-face questionnaire, convenience sampling plan, and involving a
specific target of respondents for particular logistics services. Those who were experienced logistics
services from XYZ company at least twice within a year have been selected as the potential subjects.
Items in the questionnaire were made based on the literature review, interview with actual users, and
personal observation.
Fig. 1. The Framework of KE, Kano and QFD for logistics services
Case study on IT-based application logistics services
A case study on IT-based application supporting logistics services in Surabaya has been taken. It
was called XYZ. A hundred fifty seven actual respondents were involved in the study. The subjects
were those experiencing services of XYZ with a period of August – October 2016. They were 54%
female and 46% male, with a majority of aged 16 – 25 years (55%), followed by 26 – 35 years
(27%), 36 – 45 years (14%), and above 45 years (at about 4%). Mostly they were college students
(39%), and followed by professionals (31%), entrepreneurs (17%), and the rest were housewives.
Referring to discrepancy between perceived and expected logistics services, service gap has been
calculated in each of logistics service attributes (as shown in Table 3). Afterwards, in order to
confirm that the gap was significant, the t-test for comparing two sample means has been done. The
results of t-test are provided in Table 3 as well. It shows that, in all service attributes, H0 was
rejected. It means that, to all logistics service attributes, the perceived service was less than the
expected one. The customers felt that what they have received was not met with what they have
expected.
Table 3. The statistical test for logistics service gap
No
1
2
3
4
5
6
7
8
Logistics service attributes
Vehicle type
Cleanliness of vehicle
Driver performance
Completeness of driver’s attributes
Driver rating score
Web-based application interface
Cleanliness of helmet for customer
Provision of mask
Gap*
Tangibles (T)
-0.56
-0.86
-0.65
-0.76
-0.37
-0.39
-1.27
-1.25
tvalue
pvalue
-7.733
-11.852
-8.664
-9.390
-4.984
-4.804
-15.092
-14.352
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
<
<
<
<
<
<
<
<
α
Remark
0.05
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
9
10
Food receipt
Appearance of foods ordered
-0.66
-8.399
-0.90 -10.524
Empathy (E)
11 Provision of apology once any mistakes
-0.71
-7.853
12 Confirmation for any unavailability of orders
-0.74
-9.432
13 Confirmation for any cancellations
-0.97 -12.590
Responsiveness (R)
14 Friendliness of driver
-0.66
-8.758
15 Politeness of driver
-0.82 -11.067
16 Promptness of delivery
-1.01 -11.221
17 Confirmation for any orders made
-0.47
-5.543
18 Knowledge of driver for any interesting places
-0.99 -12.256
Reliability (Re)
19 Accuracy of payment
-0.78
-9.349
20 Accuracy of driver identity
-0.79
-9.831
21 Accuracy of promotions
-0.55
-7.361
22 Accuracy of orders
-0.81 -10.925
23 Safety
-0.87 -13.978
Assurance (A)
24 Driver traceability
-0.81 -10.000
25 Warranty for orders
-0.67
-7.578
26 Privacy for customer
-0.76
-9.630
*the difference between perceived and expected service
0.000
0.000
<
<
0.000
0.000
0.000
<
<
<
0.000
0.000
0.000
0.000
0.000
<
<
<
<
<
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
H0 rejected
H0 rejected
0.05
H0 rejected
H0 rejected
H0 rejected
0.05
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
<
<
<
<
<
0.05
H0 rejected
H0 rejected
H0 rejected
H0 rejected
H0 rejected
<
<
<
0.05
H0 rejected
H0 rejected
H0 rejected
Perceived logistics services influenced particular Kansei (i.e., emotional satisfaction). In this
study, there were 10 Kansei identified, formulized and measured, i.e., helped (mean = 4.09), trusted
(mean = 3.93), secured (mean = 3.91), comfortable (mean = 3.85), innovative (mean = 3.83), friendly
(mean = 3.79), precise (mean = 3.70), professional (mean = 3.57), prompt (mean = 3.44) and cheap
(mean = 3.32). The distribution of perceived Kansei scores is shown in Fig. 2.It shows that Kansei
“helped” has the highest perceived score; it means that in general, the customers felt “helped” once
they have received logistics services from company XYZ.
4,5
4
3,5
4,0955 3,9299
3,9108 3,8535 3,828 3,7898
3,7006 3,5732
3,4395 3,3248
3
2,5
2
1,5
1
0,5
0
Fig. 2. Distribution of perceived Kansei in logistics services
Afterwards, through Kano categorization process, with respect to attractive [A] and onedimensional [O] categories, those logistics service attributes belong to were identified. They were
then connected to significant Kansei through linear model test, and calculated their satisfaction
scores. According to Hartono & Tan [16], the importance weight was determined by incorporating
the value of satisfaction score, Kano weight [see [29]], and Kansei score. The higher the importance
weight, the more important the service attribute is. The result is shown in Table 4.
No
Code
1
T2
2
T6
3
T7
4
T10
5
E11
6
E13
Table 4. The importance weight of logistics service attributes
Logistics service
|Satisfaction Kano
Kansei score
attributes
score|*
weight
Cleanliness of vehicle
3.47
A 4
Secured
3.91
Friendly
3.79
Web-based application
1.58
A 4
Innovative
3.83
interface
Helped
4.09
Professional 3.57
Innovative
3.83
Cleanliness of helmet
5.73
O 2
for customer
Cheap
3.32
Precise
3.70
Appearance of foods
3.84
A 4
Helped
4.09
ordered
Provision of apology
3.06
A 4 Comfortable 3.85
once any mistakes
Confirmation for any
cancellations
4.16
O
Helped
4.09
Comfortable
3.85
Trusted
Friendly
Prompt
3.93
3.79
3.44
Importance
weight**
54.27
2
74.00
165.25
62.82
47.12
66.07
7
8
R14
R15
Friendliness of driver
Politeness of driver
2.95
3.69
O
O
2
2
9
R16
Promptness of delivery
4.52
A
4
10
R18
4.24
A
4
Precise
3.70
62.75
11
A24
Knowledge of driver for
any interesting places
Driver traceability
3.44
O
2
A25
Warranty for orders
2.74
A
4
4.09
3.93
3.91
3.85
28.14
12
Helped
Trusted
Secured
Comfortable
*|satisfaction score| = (perceived – expected) x importance level of service
**importance weight = |satisfaction score| x Kano weight x Kansei score
5.9
29.00
130.71
128.12
120,00%
180
160
100,00%
140
80,00%
120
100
60,00%
80
Importance of What
% Kumulatif
40,00%
60
40
20,00%
20
0,00%
R14
A24
R15
E11
T2
R18
T10
E13
T6
A25
R16
T7
0
Fig. 3. The Pareto chart of logistics service attributes based on importance weight
With regard to the Pareto chart as shown in Figure 3, there were 7 logistics service attributes
deemed to be critical, i.e., T7 (cleanliness of helmet for customer), R16 (promptness of delivery),
A25 (warranty for orders), T6 (web-based application interface), E13 (confirmation for any
cancellations), T10 (appearance of foods ordered) and R18 (knowledge of driver for any interesting
places). Using a House of Quality (HoQ), some related design specifications (known as metrics) have
been formulized, as shown in Figure 4. It shows that the most critical improvement idea was that the
provision of modular system for helmet (inside and outside part) for the customer.
Discussion
This study was actually done as the extension of previous research on KE, Kano and QFD applied
in services (see [24]). By taking a case on logistics services, this study is hoped to contribute on the
efficiency of logistics performance. The field of logistics services becomes a potential niche to
explore. There is a huge market for logistics in Indonesia in year 2004 – 2014, and it becomes larger
and larger due to the growth of infrastructures and economic development. Moreover, with respect to
customer point of view, third-party logistics (3PL) was chosen since this kind of services can add a
great value to customers and companies [3].
With regard to the potential development of emotional-based service quality tools and potential
needs for 3PL services, this study has been conducted. It proposed a framework of KE, Kano and
QFD applied to one of popular IT-based supporting logistics services in Surabaya. Basically, this
company provided services on logistics either for foods, documents or passengers.
According to the research findings, it has been shown that the attribute “cleanliness of helmet for
company” was the most important one, which had significant correlation with Kansei word
“professional, innovative, cheap, and precise”. Given a very limited time, effort, budget or other
resources, the company should focus more on the cleanliness of helmet and its supporting facilities in
order to gain more customer emotional satisfaction. Though, the Kansei “helped” was of to be the
highest rated emotion experienced by the customers. It was influenced by the performance of
attribute “web-based application interface”, “appearance of food ordered”, “confirmation for any
cancellations”, and “driver traceability”. In other words, in general, the customers felt helped once
they were served by the XYZ company.
It is, also, suggested that the modular system for helmet (inside & outside part) was proposed. It
was deemed as the most prominent improvement, and then followed by the provision of application
software to give comment and rating anonymously, and also the use of application software for
confirmation and cancellation.
Conclusion, limitation and further recommendation
Conclusion. This study promotes the major role of human factors, especially Kansei, in
influencing the efficiency and effectiveness of logistics service design and development. The
integrated model of KE, Kano and QFD provides the understanding of what should be considered
and executed by the service manager or provider in improving the services offered, yet still focusing
on the prioritized solutions, given very limited resources. In this study, the improvement on helmet
system, and the provision of application software for submitting comment and rating, and doing
confirmation/cancellation were rated as high priorities.
Limitation and further recommendation. This study is of limited by a relative small sample
size, and a specific IT-based logistics services. Since the proposed model is meant to be a general
model for the improvement of logistics services, more empirical studies are required. Moreover, the
exploration on another part of logistics services is needed, not only 3PL which focuses on the end
customers, but also on more upstream entities.
Importance weight
165.25
R16
130.71
A25
128.12
T6
74
E13
66.07
T10
62.82
R18
62.75
The integration of Google Maps to the ordering system
The use of multifunction bag
The use of apps for order confirmation and cancellation
The thematic design of apps (i.e., Christmas, New Year)
The app for anonymous rating and comment
The app used by driver to order directly to vendor
The modular system for helmet (inside & outside part)
WHAT(s)
Attribute
T7
HOW(s)
9
9
3
9
3
9
3
3
9
9
9
Total weight
1487.25
1176.39
1351.29
666
1371.12
565.38
786.75
Percentage
20.09%
15.89%
18.25%
8.99%
18.52%
7.64%
10.63%
Fig. 4. The simple form of HoQ for IT-based logistics services improvement
Acknowledgement
This study on the model development and its application on logistics services were fully supported by
the research grant under the scheme of applied product with a contract number of 22/SP-Lit/LPPM01/Dikti/FT/V/2017, endorsed by the Directorate of Higher Education, the Ministry of Research,
Technology, and Higher Education, Republic of Indonesia. Also, this was partially supported by the
Department of Industrial Engineering, University of Surabaya, Indonesia.
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