15th ANQ 2017 page no. 16-26

1. Evaluating Information for the Purpose of Disseminating Innovation
Takeshi KANEKO1
, Yu KIKUCHI2
, Shin-Ichiro YOKOYAMA2
1
Tokyo City University, tkaneko@tcu.ac.jp
2
Tokyo City University
Abstract: Creating innovation in a firm is an important management strategy because
innovation can yield huge profits. However, corporate innovation creation activities are
difficult, and the current market success rate is less than 30%. Therefore, we propose a
method to evaluate information about innovation and clarify the information that a firm
should distribute in order to disseminate innovation. The existence of a chasm is an
obstacle to the spread of innovation. A chasm is the stagnation of the popularization that
occurs when the market changes from innovation to following due to the progress of
innovation. To get beyond a chasm, Sakai and Kawai recommend activating cross-
market communication between innovators and followers. They demonstrated the
importance for innovators to provide information that interests their followers. In other
words, in order to cross a chasm, firms should send information to enable the exchange
of information between innovators and followers. Therefore, this study aims to clarify the
information that the company should send to satisfy followers’ information requests.
Key Words: Innovation, Chasm, Innovator, Follower
1 Introduction
In modern Japan, innovation promotion activities nationwide such as "long-term strategic policy"
Innovation 25 "are done. Innovation is not limited to technological innovation, it is to incorporate
completely different new ideas and mechanisms, create new value, and cause a major social change.
Creating innovation for companies is regarded as an important management strategy, and it is tackled
by various domestic and external motives such as the starting advantage and depreciation. However,
corporate innovation creation activities are difficult, and the current market success rate of market
introduction success is less than 30% [1].
An obstacle to the spread of innovation is the occurrence of chasm in the innovator theory (Fig. 1).
The theory of innovation is that with the spread of innovation, the market changes from customers
(Innovators, Early Adopters) who take a positive attitude towards innovation to customers who take
passive attitudes (Early Majority, Late Majority, Laggards). In addition, the chasm is the long-term
slump of the spread that occurs when customers who make up this market change [2].
Therefore, companies are taking various measures such as creating hall products and innovation
spreading strategy to surpass this chasm. Among them, information dissemination to followers
(customers after Early Majority) is regarded as important for exceeding the chasm, and corporate
advertising activities are active year by year. However, the follower does not show a positive attitude
towards innovation, and it is difficult for companies to grasp the information that the follower is
seeking. Also, innovation itself cannot be clearly grasped by its nature. Under these circumstances, it
is currently unclear what kind of information should be transmitted by companies to disseminate
innovation.
1

2. Therefore, in this research, we aimed to clarify what kind of information the market is interested in
and to clarify information that companies should send.
Figure 1 Innovator Theory
2 Previous Research and Procedure of This Research
2.1 Previous Research
Prior research on the dissemination of innovation states that the relationship between "innovation",
"innovator" and "follower" is important when exceeding the chasm. Sakai et al. [3] modeled
popularization of innovation using multi-agent simulation and gave an implication of the innovation
diffusion strategy by performing sensitivity analysis on external variables that determine its behavior.
Sakai and others said that in order for innovation to cross the chasm, it is necessary to further activate
cross-market communication between innovators and followers. As for innovation, by providing
informers with much information on which the follower is interesting appropriately, the follower
diffuses the information to other followers and exceeds the chasm respectively.
Also, there is an information request [4] as a measure of market interest. Information request is
"desire to obtain necessary information", and innovator and follower have different information
requests respectively. The innovator has a "formalized request" and it is a clear state which
2

3. information is wanted. A follower has a "conscious request", and it is a state in which the desired
information can only be expressed in an ambiguous language.
From the above, in order for companies to disseminate information to disseminate innovation, it is
necessary to clarify "information that the innovator transmits" and "conscious request that the
follower has". For that purpose, it is necessary to comprehensively express information on innovation
and create an evaluation model of information by clarifying the information request which the market
has.
2.2 Research Procedure
The purpose of this research is to clarify information to disseminate innovation. For that purpose, we
first set up information sources to comprehensively express innovation. Second, how to express
innovation and. We clarified the information request. Third, we created an evaluation model of
information on innovation. Finally, we verified whether the proposed method of this study is valid.
3 Research Results
3.1 Survey on Innovation Information
In order to express innovation it is necessary to express changes in information due to progress of
innovation. In view of this, since the Internet is the main information search activity in recent years,
we have been using search engine optimization (SEO) site to search "search query" and "search
volume" used in information search activities on the Internet We gathered. In addition, this time we
collected data on cases of smartphones, which is a rapidly popular innovation in recent years.
In order to observe the change of information due to progress of innovation spread with the collected
search query data, we divided the collected search data by year and created co-occurrence network
and correspondence analysis (Fig. 2).
Figure 2 Co-occurrence Network Diagram for “Smartphone”
before Chasm (2008) and after Chasm (2015)
In the 2008 co-occurrence network, co-occurrence among queries is small, and the query itself shows
that there are many professional things. This is a user who collects expert information pinpoint, and it
represents "informed request" possessed by the innovator. In the co-occurrence network of 2015, co-
occurrence between queries is large, and the abstraction degree of the query itself is also high. Many
3

4. users collect information widely and shallowly, and it represents a "conscious request" of the
follower.
Figure 3 Result of Correspondence Analysis
for Query Words and Annual Year
In the results of correspondence analysis, we can observe changes in trends of search queries around
2010 and around 2011. Since it is said that smartphones have crossed the valley of popularization in
2011, we request market information requests from search queries I thought that it was possible to
express it.
Therefore, in this research, "information request" is expressed by search query, and we decided to
express "innovation" by using text data on the Internet returned as a result of search.
3.2 Collect Search Query Information
In order to comprehensively express information on innovation, we gathered Internet news using the
Asahi Shimbun Kikuzou Ⅱ vision. Also collect search queries to express information requests.
Similarly, Internet news and search query data were collected from June 2014 to June 2016 for
"Smartphone" as an example of innovation as well.
3.3 Representation of Information and Information Request by LSA
The collected text data and search query data are expressed using Latent Semantic Analysis (LSA).
LSA is to use a vector space model to generate sets of concepts related to documents and groups of
terms contained in them. In order to execute the LSA, a word document matrix At × d is created from
the collected text data. A word document matrix is a matrix in which words appearing in a document
are arranged in rows and all documents are arranged in columns. In this case, we narrowed down to
nouns with total occurrence frequency of 10 times or more, and the word document matrix At × d of
the whole document created was a matrix of 56197 × 6378.
4

5. Next, singular value decomposition (Fig. 4) is performed on the word document matrix. In this time,
the cumulative sum was calculated until the ratio to the total sum of the singular values was 0.2, and
the dimension was reduced. As a result, the high dimensional data becomes a 22 dimensional
semantic space.
Figure 4 Words Documents Matrix
Next, the collected query data is totaled for each month, and a word document matrix is created using
only the words constituting the semantic space of the month. We also call this data a query document.
By projecting the created query document onto the semantic space, the information request can be
expressed in the semantic space.
Figure 5 Model of LSA (Latent Semantic Analysis)
In order to clarify the article satisfying the information request, the similarity between the query
document on the semantic space and each document was measured using the cosine similarity (1).
Articles on the Internet increased as the date progressed, thinking that the semantic space also
changed. Therefore, we create a semantic space for each month and measure the similarity of articles.
Fig. 6 shows the similarity degree of the article "information folder" with respect to the query
document every month, the similarity with the query document changes due to the change in the year
and month, and even after the date of publication the similarity with the search query Improvement
can be confirmed.
5

6. Figure 6 Example of Transition of Similarity
3.4 Representation of Information by LDA
The semantic space by the LSA cannot interpret the axis and it is not known what kind of innovation
information should be sent. Therefore, clarify the article to be evaluated by considering the latent
topic model (Latent Dirichlet Allocation, LDA).
A topic model is a method of extracting topics that are topics from a large number of document sets
without manual intervention. In LDA, we model the generation process of the frequency distribution
ignoring the order relation of words called Bag-of-Words, estimate the topic distribution θ for each
document, and estimate the word distribution Φ for each topic. By using these, we clarify topics of
each document.
Based on the axis of the LSA, we assumed 22 topics and hyper parameters α and β of Dirichlet
distribution assumed 0.1, respectively, and LDA was executed 100 times.
The word distribution Φ generated is 6378, and the topic distribution θ is 22.
We interpreted topics from words with high occurrence frequency using word distribution in each
topic. In topic 1, words such as "people, voting, security, newspaper, net, bills" appear at the top, so
topic 1 is considered to be the topic "politics on smartphones".
Also, paying attention to the topic distribution possessed by the document 1, it can be seen that the
topics 1, 5, and 19 appear frequently. Toc 5 is "Educational Relationship", Topic 19 is a topic on
"Application". Therefore, the document 1 can be interpreted as "information related to education
using a smartphone application". In this way, in the topic model, we interpreted each document by
creating topic distribution and word distribution.
3.5 Representation of Information by DTM
LDA cannot see the transition of the word distribution and time series of topic distribution. Therefore,
it is impossible to observe the information change with respect to the information request. Therefore,
information on innovation was expressed using DTM (Dynamic Topic Model) which extended LDA
[5].
DTM was executed under the same condition as LDA. The transition of the topic distribution is as
follows.
From the results of DTM, it is possible to observe the change of the topic from the transition of the
topic distribution θ. In the case of smartphones, we cannot observe large changes in the topic
6

7. distribution. This is thought to be because the collection period of the text data is after the smartphone
has been fully popularized.
In this way, by using DTM, we can observe "change of topic" with "understandable form" of
information. Therefore, in this study, information was expressed using DTM.
3.6 Comparison of Innovation Information
By comparing the categories of innovation information, we have grasped the characteristics of
information that popular innovation has.
This time we targeted next generation cars, hybrid cars and electric cars, and PS4 and WiiU as next
generation game machines. We collected text data for each innovation and executed DTM.
As a result of comparing information on innovation by DTM, it was found that there is a difference in
topic distribution θ of two innovations at a certain time. The difference in topic distribution θ indicates
that there is a difference in information transmitted to the market for each innovation.
There was also a significant difference in the variation rate Δθ of the topic distribution. The topic
variation rate Δθ represents a change in topic on innovation. Therefore, it became clear that there is a
difference in "topic itself" and "topic change" in the information on innovation.
Based on these characteristics, it is hypothesized that "information including appropriate topics on
innovation" or "information that makes topic fluctuations high" is important as information to be
transmitted by companies. That is, the former can evaluate the information whether the information is
"an appropriate topic" or the latter is "a change in topics". Therefore, in this research, the former
"topic" was used to evaluate the information.
3.7 Evaluate Model of Information
In order to evaluate information on innovation, text data on innovation and query data were collected,
topic distribution was estimated by DTM, and similarity between query documents and documents
was measured by LSA. Also, each document having a similarity score of 0.45 or more was evaluated
as positive information for information request, and each document was evaluated with 1 being
regarded as 0.
Logistic regression analysis was performed using similarity as the objective variable and topic
components included in each document as explanatory variables.
From the created regression model, it can be seen that topic 5 (game) and topic 15 (application) have a
positive influence on market information request. In this way, it is possible to evaluate topics that are
required at a specific time.
In this research, textual data on innovation is expressed using DTM, and by expressing information
request of market with query document, we created an evaluation method of information satisfying
user's information request. By using this evaluation model, information (topic) to be transmitted by
the enterprise is clarified.
4 Study on Validity of Evaluation Model
4.1 Method of Verification
7

8. We evaluated whether the evaluation of the innovation information by the evaluation model proposed
in this research is valid. What I covered this time is the 10 items that are regarded as innovation in
recent years.
As for the verification method, like the evaluation model creation process in Chapter 3, text data and
search queries were collected for each innovation, the information was expressed by the DTM, the
information was evaluated by the LSA, and the logistic regression analysis was performed. The
discriminant predictive value based on the created regression model was calculated and verified.
4.2 Results
The average discriminative predictive value of the regression model in each innovation is 0.75, and it
is considered that information can be sufficiently evaluated in other innovations. Therefore, the
evaluation model prepared by the procedure of this research is considered to be valid as the evaluation
method of information on innovation.
5 Conclusion and Future Study
5.1 Conclusion
In this research, by collecting text data on innovation and using DTM, expressing the information of
its innovation as "topic (topic)" or "change of topic", formulation of innovation in a form that human
beings can understand went.
Also, by expressing market information request by creating "query document" and plotting on the
semantic space created by LSA, it is possible to evaluate whether each document is positive for
information request It became possible. By using these data, we created a model that "evaluates"
information on innovation in an "understandable form". By using this evaluation model, companies
can make appropriate information dissemination to the market.
5.2 Future Study
By using the evaluation model created in this research, it is possible to clarify information for
disseminating innovation. However, since various types of media and targets exist in information
transmitted by companies, it is necessary to consider more concrete information transmission
methods. Therefore, in addition to the information expression handled this time, it is thought that it is
necessary to define the expression of the media and the object.
Finally, the information topic is entirely dependent on the innovation in question. For that reason, it is
important to create a topic rather than thinking about information to be sent like this time in products
without topicality. In this research, we focused on "topics" included in documents to evaluate
information, but focusing on "fluctuation of topics", companies have caused a change in topics on
innovation and the market wants I think that innovation can be created. Therefore, in order to
disseminate innovation, it is thought that it is necessary to approach from both creation of hall product
by innovation · marketing and management of innovation by dissemination mechanism.
8

9. Bounce rate estimation model for product listing advertisements
Shotaro Fujii1
, Takeshi Kaneko2
1Tokyo city university, Tokyo, JSQC, g1781812@tcu.ac.jp
2
Tokyo city university, Tokyo, JSQC
Abstract: When consumers did bounce, advertisers should pay a wasteful cost for product
listing ads. But, consumers often did. Because it is very difficult for advertisers to understand
search intent from search keywords.
The purpose of this study is understanding consumers’ search intent from their search
keywords, and proper landing page from which consumers didn’t bounce based on their search
intent. This study also make an estimate model for bounce rate.
From careful observation of consumers’ search activities, we got 102 kinds of search intent
and strongly related search keywords for each. Those were integrated by affinity diagram into
18 categories. Search keywords were also integrated in the same way. The 18 categories of
search intent were integrated again by cluster analysis.
Next, we carried out questionnaires for evaluating bounce rate. Logistic regression analysis
was performed for each of the integrated search intent to make model.
We examined validity of our models. Two landing page were compared. One was an original
landing page, and the other was the landing page which must be less bounce rate based on our
model. We could confirm that bounce rate between the two pages had a statistically significant
difference.
Keywords: Landing page, First view, Internet advertisement
1 First
1.1 Study background
According to Toda(2010) , in recent years, the market size of Internet advertisement is expanding due
to the fact that targeting to consumers is possible and advertisement effectiveness is easier to measure
compared to mass media.
Listing advertisement refers to an advertisement displayed in conjunction with search results of
Yahoo!, Google, etc. and can change advertisement based on the searched keyword.
When operating listing advertisements, bounce rate is the index that you need to worry about. This is
because unnecessary advertisement costs will be incurred for advertisers due to bounce. However,
since the search keyword and the landing page are set at the same time in the listing advertisement, the
landing page must be created by estimating the information required by the consumer from the search
keyword. Currently, it is difficult to estimate information which consumers demand from search
9

10. keywords. As a result, consumers do bounce. Bounce means that a user visiting a web site leaves the
site by looking at only the first page to be entered, a landing page mean that the page that a visitor first
accessed via search results, advertisements, etc.
1.2 Purpose of study
This study grasps the characteristics of search keywords used from search intention. Also, by
examining what kind of information the consumer is seeking from the search intention, we grasp the
contents of the landing page where no bounce is made on the search keyword. Then, it is to create a
model that can predict the bounce rate. As a cause of bounce, it often happens that the information you
want is not on the first view of the landing page. Therefore, in this study, we focus on information that
should be placed on the first view of the landing page. First view is the screen area of a browser that
can be seen without scrolling when accessing a web page.
1.3 Previous study
According to Hirokwa(2007), he apply a concept graph theory to the search log, and propose a
method to find suitable keywords in listing advertisements. In order to solve the problem that it is not
sure whether it leads to conversions, Hashimoto(2010) use data that used listing advertisements and
propose keyword groups that are converting. Matida(2013) approached everything that needs to be
registered during ad operations in order to lead to conversions.
Hirokwa(2007), Hashimoto(2010) payed attention only to the key words. Matida(2013) focus on,
keywords, ad text, landing page, conversion, but they did not pay attention to detail about information
should be posted on landing page.
In this study, in order to reduce the bounce, we clarify the information that consumers demand for
various retrieval intentions and grasp the contents of the landing page where bounce does not occur.
2 Interview survey
We conducted an interview to extract "search intentions" consumers have about gathering
information on the Internet and "search keywords" typed in search engines. Interview method was that
we asked respondents to imagine the product group they want. And we asked respondents to take
actions ranging from purchase thought to purchase on the search engines.
The question items are "search keyword" and "what kind of information do you want to know ?" The
number of interviews is 25 male and female from 10s to 40s.
From the interview survey, 102 kinds of set of search keywords and search intention were extracted.
3 Associating search intent with search keywords
3.1 Abstraction method of search intention
Before abstracting search intent we first defined the abstraction level of the product. Because
there are some abstractions level in the same product group, I thought that abstraction was
difficult. Taking a camera as an example. There are some abstractions level like camera →
single lens reflex → Canon single lens reflex → EOS - 5D (Canon 's product).
The result of defining the product abstraction level is shown in Table 1.
10

11. Table 1 Definition result
Considering the defined product abstraction level, 102 kinds of search intention were abstracted by
affinity diagram. As a result, we were able to group search intent into 18 types.
3.2 Abstraction of search keywords and association with search intention
First of all, we abstracted the word itself of the search keyword by affinity diagram.
Table2 Word abstraction result
Next, the search keywords used for each abstracted search intention were categorized. After that,
abstraction was done for each classified search keyword, and the search intention and search key word
were related.
11

12. Table3 Associating search intent with search keywords
4 Grouping of search intent by questionnaire survey
4.1 Purpose of grouping
We further grouped 18 types of search intention and search keywords abstracted by affinity diagram.
The reason is that when the advertiser actually operates the listing advertisement, it is difficult to prepare
18 kinds of suitable landing page for 18 kinds of search intention. However, even if abstracting 18 types
of search keywords further by affinity diagram, the abstraction degree becomes too high, which makes
it difficult to handle. Therefore, for 18 kinds of search intentions, data showing whether or not to return
immediately by showing the landing page is collected. Then group search intent showing similar
reaction. The reason for using this method is that when you showed the same landing page even if the
search intention was different, if you did not go back to bounce, you thought that you do not need to
handle those search intents separately. Data collection is done using a questionnaire.
4.2 Questionnaire method
We set scenes for each of 18 types of search intentions and ask respondents to imagine. We prepare
12

13. some landing pages in advance, show 5 types of them and ask them to answer whether they should go
bounce or not. From the problem of this study, show the first view of the landing page and ask them to
answer.
4.3 Create a questionnaire
When preparing several landing pages, I was careful not to keep the information on the same. In doing
so, we entered some appropriate search keywords into the search engine and investigated the existing
landing page. As a result, 10 kinds were prepared in consideration of the information on the existing
landing page.
4.4 Consideration of questionnaire data
We collected search intent as variable, landing page as sample, number of not bouncing as data. As a
result, if the search intention regarding the product abstraction level 3 is not the landing page related to
abstraction level 3, the bounce will occur with a high probability. For example, for search intention "I
want to know the price of EOS - 5D (Canon camera)", a bounce will happen unless it is a landing page
related to EOS - 5D. From this result, the search intention regarding the product abstraction level 3 is a
stage in which purchasing willingness is high and he / she is not sure whether to buy or buy a product.
Therefore, we will exclude it in this study.
4.5 Analysis of questionnaire data
We group search intents other than product abstraction degree 3 by Ward's method of cluster analysis.
The results are shown in Table 4.
Table 4 Grouping result
13

14. 5 Creating a bounce rate prediction model
The bounce rate is predicted by a regression model. In order to create a model that predicts the bounce
rate for each of the six group, tally the data separately for each cluster. In order to determine the
explanatory variable, we examined 50 existing landing pages and organized what kind of information
was posted. As a result, we were able to summarize it into nine, so we use these as explanatory variables.
The landing page was evaluated as 1 when "Information" as an explanatory variable was posted on the
landing page, and 0 when it was not posted. The objective variable is the bounce rate.Logistic regression
analysis was performed on these six group.
6 Verification
6.1 Method of verification
In this study, we grasped proper content of landing page from which consumers didn’t bounce based
on their search intent. Therefore, show the existing landing page and the landing page derived from this
study, compare the bounce rate, if it is up, make it validation success.
6.2 Verification result
The verification results are shown in Table 5.
Table 5 verification result
From the results of hypothesis Testing for the Difference in the Population Proportions, significant
difference was confirmed except Group 2. Therefore, except for Group 2, we believe that the result of
this study is highly reliable.
The reason why Cluster 2 was not rejected is that these search intentions are retrieved at the initial
stage of the information gathering stage, and the range of desired information is wide. As a result, we
can not confirm the difference between the two populations.
7 Conclusion and future issues
7.1 Conclusion
In this study, we made clear the search intention that consumers hold when collecting information by
interview and the search keywords used at that time. Also, show the actual landing page, collect data on
whether to do bounce or not, we grasped proper content of landing page from which consumers didn’t
14

15. bounce based on their search intent. Then, a bounce rate prediction model was created
7.2 Future tasks
We were able to grasp the content of the landing page that will not be bounced, but we could not make
suggestions until the method of creating the landing page.
Also, it is important how to increase conversions for listing ads. However, in this study we focused
bounce, and we could not deal with how to induce conversions.
In the future, we need to advance study on how to create a landing page and how to induce conversion.
References
[1]Atushi Toda(2010)” The Historical Transition of Internet Advertisement in Japan and Its Nature (Part
1)” Information Processing Society Journal Vol. 17, No. 1 No. 49-54
[2]Shingo Iwakiri, Satio Hirokwa(2007)” Proposal of keyword finding method in keyword linked
advertisement” Information Processing Society Research Report Database System, 195-199, 65 (2007 -
DBS - 143)
[3]Akane Hashimoto(2010)” Keyword proposal considering purchasing model in listing advertisement”
[4]Keisuke Matida(2013)” Proposal of effective conduction lead with consideration of user's intention
in listing advertisement”
15

16. Development of Tripe Snack and Assessment of Consumer Satisfaction by
Kano Model
Rayamajhi Sherpa, D.*1
, Ghimire, L.1
, Subba, D.2
Gautam, RR.2
1
National College of Food Science and Technology, Tribhuvan University, Kathmandu
2
Central Campus of Technology, Tribhuvan University, Dharan
*Corresponding author: dawarayamajhi@gmail.com
Abstract
One of the popular and useful models for the integration of customer’s voice into the future
processes of new product development is Kano model. Kano model directly helps in new product
design by incorporating the preferences of customer. A study was conducted for the preparation
of spicy ready-to-eat tripe snack from buffalo tripe with two different spice mixtures. A tripe
snack is a shelf-stable meat product developed from the intestinal by-product of cattle, tripe. The
only difference in the spice mixtures is that one mix will contain the Schwann Pepper (timur) but
another will not contain it. The new product was developed and the consumer preference was
tested on the four parameters. Sensory analysis was performed using nine point hedonic scale
method and the values were converted and calculated according to the Kano model to measure
the consumer satisfaction on four different parameters of both samples. The four parameters are
color, odor, texture and taste. The Kano model helped to analyze the parameters and its role on
the consumers’ satisfaction and or dissatisfaction for new product. Microbiological stability of
the product influenced by the presence of the timur was studied and it was found that the effect
of timur on the microbiological stability was significant at (P<0.05). From the evaluation, the
‘taste’ of snack got the highest positive coefficient of 0.96 and was considered as satisfactory
parameter whereas ‘texture’ of snack got the highest negative coefficient of -0.86 and is
considered as important factor to be improved.
Keywords: Consumer satisfaction, Kano model, Sensory Analysis, Tripe Snack
Introduction
Tripe is the abdominal part of the cattle which is generally considered as by-product.
Different types of value-added food product can be prepared by the tripe in order to utilize the
by-product and get economic return from it. Tripe snack is one unique type of spicy ready-to-eat
meat product which is shelf-stable at room temperature 27°C for 9 months.
It is never enough to find the importance of different product attributes in the competitive
environment. To follow the changes to consumers’ need and to evaluate the current product
competition is also important. Assessment of the customer satisfaction and or dissatisfaction of
newly developed product (Tripe Snack) with the help of Kano Model can be effectively done.
Different attributes has different impacts on the consumers and it changes over time. It may
be either due to movements of competitors introducing improvements, due to the fact that
customers get used to them or due to the offer of substitute products (Tontini, 2000). According
to the Kano’s quality elements should be classified into three classes depending on their ability

17. to create customer satisfaction or cause dissatisfaction: elements of expected quality, attractive
quality and one-dimensional quality (Pirttilä, 1998).
The Kano Two-Dimensional Quality Model not only proposed theoretical models, but also
practical actions. Unlike the theories that focus on exploring customer preference, the Kano
Two-Dimensional Quality Model places greater emphasis on the expectations of customers and
discusses factors that affect customer satisfaction (Jane & Dominguez, 2003).
The Kano model has emerged into one of the most popular quality model nowadays. It has
caught the interest of many marketing practitioners and researchers who are involved in the
product or service development projects and who are trying to identify those product/service
features that represents key drivers of customers’ satisfaction and dissatisfaction. One of the
major assumptions of the Kano model is that certain product/service attributes (quality elements)
primarily have an impact on creating satisfaction, while others primarily have an impact on
creating dissatisfaction (Mikulic, 2007).
Satisfied
Attractive Quality
One-dimensional Quality
Indifferent
Unfulfilled Fulfilled
Must-be Quality
Reverse Quality
Dissatisfied
Fig 1: Kano Model (Karlsson & Le, 2013)
The 5 categories of quality elements (Kano, N., Takahashi, F., Gan, 1984).
1. Attractive Quality
When fulfilled, they provide satisfaction, but when not delivered, they do not cause
dissatisfaction, because they are not expected by the customer (asymmetric impact on OCS). An
increase in fulfillment (performance/positive disconfirmation) results in an over-proportional
increase of satisfaction (nonlinear impact on OCS).
2. One-dimensional quality
Quality elements that results in satisfaction when fulfilled and elements in dissatisfaction
when not fulfilled (symmetric impact on OCS). An increase in fulfillment (performance/positive
disconfirmation) results in a proportional increase of satisfaction, whereas a decrease in
fulfillment results in a proportional decrease of satisfaction (or increase of dissatisfaction).

18. 3. Must-be quality
Quality elements that results in dissatisfaction, when not fulfilled (not delivered at a
satisfaction level) because the customer takes them for granted. But when fulfilled (delivered at a
satisfactory or higher level) they do not result in satisfaction (asymmetric impact). A decrease in
fulfillment results in an over-proportional increase of dissatisfaction (or decrease of satisfaction)
(nonlinear impact on OCS).
4. Indifferent quality
Quality elements which results neither in satisfaction nor dissatisfaction whether fulfilled of not.
5. Reverse quality elements
Quality elements that results in dissatisfaction of customer when fulfilled and satisfaction when
it is not fulfilled.
Methodology
Product Development
Fresh and sound buffalo tripe was procured from the local market. It was cleaned with water and
all the unnecessary fatty tissue and filth were removed. Tripe was then weighed and it was boiled
for 30 minutes as whole. It was boiled in water without any condiments. The shallow pan was
used for the boiling and the pan was covered with lid. After 30 minutes the tripe was checked if
it was cooked properly. The cooked tripe was then removed from the pan and allowed to cool
before cutting into desired shape and size. The cooled tripe was cut into 1x1 cm size chunks. The
chunks were again fried in the oil. The quantity of the frying oil was also measured by using and
discarding the calculated amount of oil. As the tripe turned into golden brown color the frying
was stopped and tripe was removed from the pan. All the spices were already in the powdered
form and the spice were measured according to the recipe. The spice mix was then heated in the
pan on which the tripe was fried. As the flavor of the spice start to evolve the tripe chunks were
again heated for 1-2 minutes and mixed properly. Now, the spicy tripe is ready and it is allowed
to cool before packing in the air-tight container. Measured quantity of the tripe was taken and the
required amount of timur was added to the spicy tripe. It was then considered as treated or timur
added batch. The previous batch without timur was considered as control or unadded batch.
Linear programming is a widely used model type that can solve decision problems with many
thousands of variables. Generally, the feasible values of the decisions are delimited by a set of
constraints that are described by mathematical functions of the decision variables. The feasible
decisions are compared using an objective function that depends on the decision variables. For a
linear program the objective function and constraints are required to be linearly related to the
variables of the problem (Bland, 1977).
Nutiritve Content and Price of Ingredients
Ingredients
Protein
(kg/kg)
Fat
(kg/kg)
Fiber
(kg/kg) Cost per (kg/kg)
Tripe 0.19 0.03 0 960
Spices 0.05 0.33 0.1 1055
Oil 0 1 0 120
Constrains:

19. Min Protein 0.19x+0.05y >0.2
Max Fat 0.03x+0.33y+1z < 0.1
Min Fiber 0.1y >0.01
Objective Function: Minimize P=960x+1055y+120z
Non-negativity: x, y and z
tripe spice oil constrains unit price
protein 0.19 0.05 0 0.2 960
fat 0.03 0.33 1 0.1 1055
fiber 0 0.1 0 0.01 120
1 1 1 1
Linear Solution: x=85.87%, y=10% and z=4.13%
Cost: Tripe= Rs. 824.4, Spices= Rs. 105.5 and Oil= Rs. 4.94
Total price= Rs. 935 per kg product
Assessment
Nine point hedonic scale method was applied to evaluate the sensory parameter of both snacks.
51 individuals performed the sensory analysis and gave their scores. The points given to the
control sample were considered as the score for the negative questions and the points for the
treated batch were considered as the score for the positive questions. Hedonic point scale was
symmetrically folded and 9-points were converted for 5-questions as follows with slight
modification (Linda B mabesa )
Table: 1. Conversion of hedonic scale to Kano Model
Hedonic Scale Answer type
9-8 I like snack that way
7-6 Snack must be that way
5 I am neutral
3-4 I can live with snack that way
1-2 I dislike snack that way
The conversion of the hedonic points to the Kano model was done with the help of simplified
questionnaire table the conversion was easy and logistic.
Table: 2. Question structure
Positive Questionnaires Answer type Hedonic Scale
What if the “timur” is
added on the snack?
(treated batch)
I like snack that way 9-8
Snack must be that way 7-6
I am neutral 5
I can live with snack that way 3-4
I dislike snack that way 1-2
Negative Questionnaires Hedonic Scale

20. What if “timur” is not
added on the snack?
(control batch)
I like snack that way 9-8
Snack must be that way 7-6
I am neutral 5
I can live with snack that way 3-4
I dislike snack that way 1-2
All the hedonic points were converted to the Kano Functional and dysfunctional question and the
values were evaluated with the help of table below.
Table: 3. Table of Kano Evaluation (Gailevičiūtė, 2011)
Customer
requirements
Dysfunctional (negative) question
Functional (positive)
question
Like Must-be Neutral Live with Dislike
Like Q A A A O
Must-be R I I I M
Neutral R I I I M
Live with R I I I M
Dislike R R R R Q
A: Attractive O: One-dimensional
M: Must-be Q: Questionable
I: Indifferent R: Reverse
Proximate analysis of the tripe snack was done according to the standard methods given
by (AOAC, 2005).
Microbiological analysis: All the microbiological parameters were determined by following
standard methods of (APHA, 1984).
Result and Discussion
After the functional and dysfunctional questions values were combined and evaluated and the
table of results was obtained.
Table: 4. Table of Results: The number indicates the number of the responds.
Product Parameter A O M Total Category
Color 15 2 34 51 M
Odor 31 6 14 51 A
Taste 9 40 2 51 O
Texture 7 36 8 51 O
From the above table we can clearly see that the parameters have its own importance to the
product. It was found that the color of the product is the “Must-be quality” by the Kano model.
Similarly, 31 out of 51 consumers gave the points which were converted into Kano model and
was found out that Odor is the “Attractive quality” of the prepared snack. Same thing was done
with the taste and texture parameter of the product. It was found out that both taste and texture is
“One-dimensional quality”. Now we can say that, when the product is made or similar product is

21. made then the parameters such as color, odor, taste and texture has significant influence in
product and these parameters should be considered as one of the important aspect of the product.
Indifferent quality was not found by evaluating the responds. Hence, I=0.
Table: 5. Table of Customer Satisfaction Coefficients
Product
Parameter
A% O% M% Total (A+O) / (A+O+M+I)
(O+M) /
(A+O+M+I)(-1)
Color 29 4 67 100% 0.33 -0.71
Odor 61 12 27 100% 0.73 -0.39
Taste 18 78 4 100% 0.96 -0.82
Texture 14 71 16 100% 0.84 -0.86
It is really important to calculate the customer satisfaction coefficient because it indicates how
much the product features and properties will lead to customer satisfaction or vice versa –
dissatisfaction. Summing up the average satisfaction with the impact, influence, it must be to add
attractive, one- dimensional column and the divide by the total attractive, one-dimensional, must-
be and indifferent responses. The calculation of average impact on dissatisfaction it should add
the one-dimensional and must-be columns and then divide by the same factors (Gailevičiūtė,
2011).
Extent of satisfaction: (A+O) / (A+O+M+I)
Extent of dissatisfaction: (O+M) / (A+O+M+I)*(-1)
Satisfaction of the customer can be easily identified by the help of customer satisfaction
coefficient obtained. If the positive satisfaction coefficient value is close to 1 then the parameter
has very strong influence in the satisfaction of the customer. If the value is near to 0, the
influence of that parameter is not that strong. Similarly, the negative coefficient also helps to
indicate the intensity of the dislikes. If the negative coefficient is close to -1, then there is very
strong dissatisfaction of the customer in that parameter. If the negative value approaches to 0,
then there is no significant or strong dissatisfaction.
According to our evaluation, the taste has the maximum positive coefficient value 0.96 which is
almost equal to 1. This indicates that the customer is highly satisfied with the taste of the
product. Similarly, the negative value in texture is -0.86 is the largest negative value which is
near to -1. Hence, the parameter texture was found to be strongly dissatisfied by the customer.
Reviewing and summarizing all the Kano model, (Berger, Blauth, & Boger, 1993) concluded
that the method of evaluating the customer satisfaction is perfect, but even this model still has
more or less failures and shortcomings. One of them – the answers wording to the questions must
be made very carefully and thoroughly. Second, a survey about service characteristics can help in
implementing new ideas, but do not forget that different people have different approaches and
service features and other elements, so even the Kano survey have difficulty determining what it
is to improve service and customer satisfaction in them (Berger et al., 1993).
Proximate Results

22. The moisture, protein, fat, ash, salt and crude fiber of selected product was determined as per
(AOAC, 2000) method. The sample size for protein and fat were determined using the formula
n=Z2
∝/2 .σ 2
/(e2
) and was found to be 11. The protein content of extruded tripe snack with
combination of wheat flour was found to be 17.39±0.10%, for extruded tripe with corn flour was
found to be 21.03±0.5% and for extruded tripe snack with rice flour was found to be 21.9±0.1%
as reported by (Anandh, 2013). Lower and upper limit of mean value of the protein composition
of our sample was found to be 9.93%, 10.964% respectively, for fat composition 14.758%,
15.648% respectively, for moisture 42.86%, 44.96% respectively, for ash 2.60%, 2.79%
respectively, for carbohydrate 18.61%, 23.22% respectively, for salt 5.35%, 5.75% respectively
and for crude fiber 1.17%, 1.35% respectively at 95% confidence level.
Protein content of the product
43.915
10.447
15.203
1.263 2.7
5.554
20.919
0
10
20
30
40
50
Frequency
Parameter
Proximate Composition
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
7 8 9 10 11 12 13
f(x)
x<min
x>max
8.86%
85.53%
5.61%

23. Fig: Hypothesis testing for protein
In the above figure, critical region, acceptance region and critical values for protein were shown.
The critical minimum value of protein is 8.86% and the critical maximum value of protein is
5.61% and the accepted value is 85.53%
Fat content of the product
Fig: Hypothesis testing for fat
In the above figure, critical region, acceptance region and critical values for fat were shown. The
critical minimum value of fat is 9.03% and the critical maximum value of fat is 2.89% and the
accepted value is 88.08%.
Microbiological Results
The result of the two batches was determined and was significant at (P<0.05). It was due to the
timur which has anti-microbial activity and it was incorporated in the treated sample (Rajsekhar,
Chandaker, & Upmanyu, 2012). In control sample, timur was not incorporated and hence the log
cycle is higher than that of treated sample. Hence, timur shows the antimicrobial activity on the
snacks and helps to inhibit the growth of aerobic organisms and enhance the stability of the
product. The growth of pathogenic microorganisms such as coliforms was not studied because
the presence of coliforms comes from fecal contamination which was avoided. Generally, the
safe level of the TPC for the ready-to-eat meat product is less than log 6 cfu/g. (SP, Chawl; R,
2004). Salt can preserve meat by retarding microbial growth. It retards microbial growth by
extracting water from meat and thus lowering water available for the growth of microorganisms.
Secondly, it extracts water from microbial cell and cause death by plasmolysis. In the third
possible mechanism salt ionizes and the ions diffuse into microbial cells and interfere with the
metabolism (Ingram & Kitchell, 1967).
Table 6: Microbiological result of control and treated sample.
Parameter (log cfu/g) Storage period in days
0
0.1
0.2
0.3
0.4
0.5
0.6
12 13 14 15 16 17 18 19 20
f(x)
x<min
x>max
2.89%
9.03%
88.08%

24. 0 15 30 45 60
Control TPC 3.11 4.26 5.13 5.43 6.63
Treated TPC 2.70 3.74 4.08 4.44 4.75
Fig 2: Microbiological Stability of Snacks within different storage days.
In the given above diagram, the linear equation for the control sample with respect to storage
days was found to be yc = 0.0322x + 2.9787and Rc² = 0.9226. Similarly, for the treated sample it
was found to be yt = 0.0546x + 3.2722 and Rt² = 0.9717. The above linear equation shows that
the predictive storage stability of the treated sample.
Conclusion and Recommendations
A new product was developed with buffalo tripe and spices mixture. Its sensory analysis was
done by the 51 consumers. The factor which could satisfy the consumers’ need was analyzed by
the help of Kano model. Also the factor that influences the consumers’ dissatisfaction was also
evaluated. It was found that the taste is one factor in new product development which influences
the consumers positively similarly, the texture of the new product is one important factor that has
strong influence in the consumers’ dissatisfaction. So, whenever we are trying to develop the
new food product we must consider the quality of the product on the basis of positive and
negative coefficients in order to raise the consumers’ satisfaction.
Acknowledgement
I am very thankful to NQPCN for this golden opportunity; Dr. Ekaraj Paudel (Program Head,
NCFST) for the guidance and knowledge. I would also like to thanks all the direct and indirect
helpers. Special thank goes to my college for complete facilities. The following experiment was
conducted with the help of sensory data obtained from the final year dissertation of fourth year
which is a matter of syllabus. The topic of dissertation is “Development and Assessment of Shelf
Stable Tripe Snack”. All the laboratory work was performed in the provided college facilities.
y = 0.0546x + 3.2722
Rc² = 0.9717
y = 0.0322x + 2.9787
Rt² = 0.9226
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
0 15 30 45 60
logcfu/gm
Storage days
Microbiological stability
Control
Treated

25. References
Anandh, M. A. (2013). Quality of Extruded Tripe Snack Food Incorporated With Different
Extenders and Buffalo Rumen Meat, 32(2), 139–143.
AOAC. (2000). Official Methods of Analysis Washington DC: AOAC International. (E. Horwitz,
W., Ed.) (17th ed.).
AOAC. (2005). Official Methods of Analysis.
APHA. (1984). Compendium of Methods for the Microbiological Examination of Foods.
Berger, C., Blauth, R., & Boger, D. (1993). Kano’s methods for understanding customer-defined
quality. Center for Quality Management Journal, 2(4), 3–36.
Bland, R. G. (1977). “New Finite Pivoting Rules for the Simplex Method”. Mathematics of
Operations Research., 2(2), 103–107.
Gailevičiūtė, I. (2011). Kano Model : How To Satisfy Customers ?, 4(12), 14–25.
Ingram, M., & Kitchell, A. G. (1967). Salt as a preservative for foods. Journal of Food
Technology. https://doi.org/10.1111/j.1365-2621.1976.tb00769.x-i1
Jane, A.C. & Dominguez, S. M. (2003). Citizens′ Role in Health Services：Satisfaction
Behavior：Kano′s Model, Part 2,. Quality Management in Health Care, 12(1), 72–80.
Kano, N., Takahashi, F., Gan, S. (1984). “Attractive quality and must-be quality”. Quality
Control Monthly 21(5): 33-41, translated from the Japanese “Quality” Magazine, 14(2), 33–
41.
Karlsson, M., & Le, T. H. (2013). A Review of the Kano Model, 1–10.
Mikulic, J. (2007). The Kano Model – A Review of its Application in Marketing Research from
1984 to 2006. Proceedings of the 1st International Conference Marketing Theory
Challenges in Transitional Societies, (1), 87–96.
Pirttilä, J. H. A. links open the author workspace. O. the author workspaceOpens the author
workspaceTimo. (1998). Sharpening logistics customer service strategy planning by
applying Kano’s quality element classification. International Journal of Production
Economics, 56-57, 253–260.
Rajsekhar, S., Chandaker, A., & Upmanyu, N. (2012). Spice as antimicrobial agents: a review.
International Research Journal of Pharmacy, 3(2), 4–9.
SP, Chawl; R, C. (2004). Microbiological safety of shelf-stable meat products prepared by
employing hurdle technology, 15(7), 559–563.
Tontini, G. (2000). Identification of customer attractive and must-be requirements using a
modified KANO’s Method: Guidelines and Case Study. Kanon Initialli Focused on Product
Features Not on Needs, (January 2000), 728–734. https://doi.org/10.13140/2.1.1518.2721

26. Analyzing Relationship between Social Quality as One Dimension of Product
Quality and Customer Satisfaction: A Case Study of Automobiles
Hideo Suzuki1
, Shane J Schvaneveldt2
, Mitsuki Masuda3
1
Keio University, Japan, JSQC, hsuzuki@ae.keio.ac.jp
2
Weber State University, USA, JSQC, schvaneveldt@weber.edu
3
Keio University, Japan, masuda_mitsuki@keio.jp
Abstract: In this research, a new framework for quality dimensions was proposed by including
social quality as a new factor in quality related to society/third-parties into Garvin’s eight
dimensions of product quality, and the relationship between social quality and customer
satisfaction was clarified. Hence, a case study on automobile was conducted in order to
examine the impact of social quality on a consumer product. First, the measurement scale
items for the eight quality dimensions and social quality were developed, and a hypothetical
model was produced. A questionnaire survey for the automobile quality was conducted via
Internet, and the relationship between the quality dimensions including Social Quality and
customer satisfaction were examined by utilizing covariance structure analysis. The results
showed that social quality has a negative effect on customer satisfaction in the model.
However, in the case of focusing on the hybrid engine car owners, it implied that social quality
has a positive effect on customer satisfaction.
Keywords: Covariance Structure Analysis, Customer Loyalty, Survey for Automobiles, and
Quality Dimensions
1. Introduction
In recent years, customer preferences for products and customer expectations for corporate social
responsibility have diversified and increased respectively. Companies are no longer depending on mass
marketing techniques to achieve success, and turning to one-to-one marketing techniques in order to meet
the needs of customers. Furthermore, customers are demanding that companies, as members of society,
address the issues of sustainable development and engage in environmental management practices.
Accordingly, customers’ way of thinking about product quality is changing. It is no longer sufficient to
design and market products that maximize customer/user satisfaction. Companies should develop products
that also address the needs of society, environment and other stakeholders.
In order to effectively and efficiently secure the products that satisfy the needs of customers, the product
quality should be grasped and evaluated as quality dimensions by decomposing it on the basis of various
properties. Garvin (1987) discussed that dimension of the tangible product quality could be classified into
eight dimensions: performance, feature, conformance, durability, reliability, serviceability, aesthetic, and
perceived quality, which are supported by various researchers and the foundation of discussion on product
quality.
Schvaneveldt (2005, 2011) proposed a social dimension of quality than encompasses social and
environmental aspects, and then, Kianpour (2014) discussed that the existing Garvin’s eight dimensions are
not sufficient because the dimensions focus on the only aspect of consumers. In recent years when the
concern of environmental issues is increasing, Kianpour (2014) discussed that instead of consumer’s aspect,
environment aspect should also be included in product quality dimension.
In this research, we propose a new framework for quality dimensions by including social quality as a new
factor in quality related to society/third-parties into Garvin’s eight dimensions of product quality, and
clarify the relationship between social quality and customer satisfaction. Hence, case study on automobile
27

27. need to be conducted in order to examine the impact of social quality on a consumer product. First, the
measurement scale items for the eight quality dimensions and social quality should be developed, and a
hypothetical model need to be produced. A questionnaire survey for the automobile quality will be
conducted via Internet, and the relationship between the quality dimensions including social quality and
customer satisfaction will be examined by utilizing covariance structure analysis.
The structure of this paper is given as follows: In section 2, literature review corresponding to Garvin’s
eight quality dimensions and social quality are explained. In section 3, the constructed concepts such as
eight quality dimensions, social quality, customer satisfaction and loyalty, and the development of
measurement scale items are discussed. The hypothetical model is also presented and the questionnaire
survey is explained. In section 4, hypothetical models are discussed, where the covariance structure
analysis is conducted. In section 5, conclusion is elaborated.
2. Quality Dimensions
In this section, the Garvin’s eight quality dimensions, social quality, and their literature review are
explained.
2.1 Performance
Performance is a product’s primary operating characteristics (Garvin, 1987). For automobiles, acceleration,
handling, and cruising speed are associated with performance. Companies focus on improving the
performance of their products, but the overall performance levels are difficult to develop when they involve
benefits that do not fit every consumer needs (Garvin, 1987). Therefore, the companies should continually
conduct activities from the consumers’ perspectives. Karnes et al. (1995) analyzed shirts as an example of
products to measure and classify quality on the basis of consumer viewpoints, and concluded that products
should be categorized according to the Garvin’s eight quality dimensions. Karnes et al. (1995) also showed
that as for the example of shirts, participants perceived performance as a low importance among the eight
dimensions of quality. In addition, Brucks et al. (2000) investigated how the price, brand name and product
characteristics (quality dimensions) influence the judgment for consumer purchasing. Brucks et al. (2010)
also conducted a survey by originally reconstructing six dimensions of quality based on Gavin’s eight
dimensions of quality. According to the survey results, the performance is classified as the fifth most
important dimension among the six quality dimensions.
2.2 Features
Garvin (1987) stated that features are a second dimension of quality, which is also a secondary aspect of
performance. Features are the “bells and whistles” of products, those characteristics that supplements their
basic functioning. However, functions in modern products are difficult to classify as primary or secondary
performance. For automobiles, security functions may be classified as features along the definition of
feature by Garvin (1987), but cars without a security functions do not exist. Garvin also stated that the line
separating primary performance from secondary performance is often difficult to draw. Therefore, in this
research, feature is used as functions other than primary performance.
Karnes et al. (1995) described that as for the example of shirts, features are regarded as an important
dimension of quality. On the other hand, Brucks et al. (2000) stated that the impact of features on purchase
changes depending on the information, such as price or brand name, which the consumers have gathered
before purchasing a product.
2.3 Conformance
Garvin (1987) stated that conformance is the degree to which product’s designs an operating characteristics
meet established standards. This dimension is a traditional approach to quality which is called “quality of
28

28. realization”. Although the customers are able to realize that the product is good and no defects, they may
not be familiar with conformance. Karnes et al. (1995) presented that for the example of shirts, conformance
is regarded as an important dimension of quality.
2.4 Durability
According to Garvin (1987), duration can be technically defined as the amount of use from a product before
it deteriorates or before it breaks down and replacement is preferable compared to repair. For the results
from Karnes et al. (1995) and Brucks et al. (2000), durability is relatively an important dimension of quality.
2.5 Reliability
Reliability dimension reflects the probability of a product malfunction or failing within a specified time
period (Garvin (1987)). Common measures for reliability are the mean time to failure (MTTF), the mean
time between failures (MTBF), and the failure rate per unit. Reliability generally becomes more important
to consumers as downtime and maintenance become more expensive.
Durability and reliability are closely linked but are different dimensions. Durability refers to how long the
product lasts, and after the product life time, it needs not be repaired or maintenance. Reliability refers to
how often the product fails, and measures how well the product functions with repairs/maintenances for a
specified period. As for the linkage between durability and reliability, a product that often fails is likely to
be scrapped earlier than a product that is more reliable, which can correspondingly increase the repair cost
and make the customers purchase a competitive brand product. Karnes et al. (1995) found that for the
example of shirts, reliability is also an important dimension of quality.
2.6 Serviceability
Garvin (1987) defined serviceability as responsiveness, courtesy, competence and ease of repair.
Consumers consider the time before service is restored, timeliness which appointments is kept, the nature
of dealing with service personnel, and the frequency with which service calls or repairs fail to overcome
outstanding problems. However, Brucks et al. (2000) stated that serviceability had less impact on the
purchase, because the consumers are difficult to obtain sufficient information for serviceability.
2.7 Aesthetics
Garvin (1987) mentioned that aesthetics is how a product looks, feels, sounds, tastes, or smells, which are
clearly a matter of personal judgment and a reflection of individual preference. The aesthetics dimension
differs from subjective criteria, such as pertaining to “performance”. As for the aesthetics dimension, it is
impossible to satisfy everyone. Karnes et al. (1995) presented that for the example of shirts, aesthetics is
regarded as most important dimension of quality.
2.8 Perceived Quality
Perceived quality refers to a quality image that consumers perceive. For example, it is a quality image that
they perceive from advertisement, word of mouth, brand name, etc. Like the above-mentioned aesthetic
dimension, perceived quality is based on the subjective. Garvin (1987) stated that consumers do not always
have complete information about a product’s attributes, and indirect measures may be their only basis for
comparing brands. Images, advertising and brand names, which are inferences about quality of realization,
can be critical. Karnes et al. (1995) presented that for the example of shirts, perceived quality is also an
important dimension of quality.
29

29. 2.9 Social Quality
Generally, eight quality dimensions by Garvin (1987) can comprehensively explain product quality.
However, some scholars pointed out that the change of the times tends to make eight quality dimensions
insufficient in current situations (Schvaneveldt (2005, 2011) and Kianpour (2014). Schvaneveldt (2005,
2011) proposed a social dimension of quality than encompasses social and environmental aspects, and
subsequently Kianpour et al. (2014) discussed that those eight dimensions were not sufficient because the
eight dimensions are only focused on the aspect of consumers. In recent years when concern of
environmental issues is increasing, Kianpour et al. (2014) was not only discussed on the aspect of consumer
but also the aspect of environment should be included in the dimension of quality. Hence, social quality of
a product is defined to be the degree to which the product/service meets the needs of society, including
third-parties and the natural environment (JSQC (2011)).
Furthermore, Kano (2004) briefly referred to automobile emissions as an example of a product’s effects on
humans and the environment. Yokoyama et al. (2000) defined "Socially Responsible Quality", as a quality
concept comprised of three elements; usability, environmental protection, and safety. Yokoyama et al.
(2000) argued that companies should consider the aspect of social responsible quality during designing a
product.
In addition, Juran (2004) stated that the required and essential characteristics of high quality products that
satisfy consumer’s needs are not to cause defects in use and not to harm humanity, which suggests that the
quality dimensions should include the consideration for the whole society.
In this research, we draw upon Schvaneveldt (2000, 2005) to extend these concepts of quality to also
consider impacts on third-parties other than the actual user.
3. Construct Concepts and Hypotheses
3.1 Construct Concepts
The constructed concepts regarding quality dimensions used in this research are the following nine concepts,
which consist of the eight quality dimensions by Garvin (1987) and social quality dimension:
(1) Performance (2) Features (3) Conformance (4) Durability (5) Reliability (6) Serviceability
(7) Aesthetics (8) Perceived Quality (9) Social Quality
3.1.1 Costumer Satisfactions
The concept of customer satisfaction is often used as an evaluation for products or services. Customer
satisfaction is generally defined as "emotion caused by customer perception of the difference between the
customer's expectation for the company, product/service and the degree of the corresponding achievement”.
Wilson et al. (2012) stated that there are two levels of customer's expectation: desired levels and validated
levels for product/service. If the achieved level for the product/service lies in between the above two levels,
it is acceptable. In addition, if the customer recognizes that the actual level for the products or service
exceeds the desired level, the customer is in a state of “very satisfied" with the products or services. On the
other hand, if the customer recognizes that the actual level for the products or service is lower than the
desired level, the customer is in a state of "dissatisfied". Customer satisfaction is also affected by the
expectation’s level. However, the expectation’s level varies for each customer. Thus, the companies which
are providing products or services should understand and manage the customer’s expectation to obtain
higher customer satisfaction.
3.1.2 Customer Loyalty
Reichheld (2003) defined customer loyalty as customer's intention that the customer is willing to
continuously receive or purchase products/services from the company. Customer loyalty includes the
30

30. behavioral aspect of whether the customer actually purchases and uses the company's products/services,
and the attitude aspect such as loyalty, love, affection, and affection, which the customer feels with the
company or its company's products/services.
3.1.3 Development for the Measurement Scales and Items
For the case study of automobile, we developed the measurement scale items for the construct concepts:
Garvin's eight quality dimensions (51 items), social quality dimension (9 items), customer satisfaction
dimension (4 items), and customer loyalty dimension (6 items). Their items were developed by using
existing related literatures and ideas as well as opinions from scholars and experts. For experts, professors
from two universities who major in quality management research field and three employees who work and
have sufficient experience at a major automobile company were involved in suggesting relevant
information in customer survey. We aim to develop highly sophisticated questionnaire items by embedding
both academic and practical aspects. Thus, Table 1 shows the constructed concept, the number of
measurement scale items, and the appropriate references. In Table 2, 9 measurement scale items for social
quality dimension are also presented. For each item, 10-point Likert scale was used, and the respondents
were asked to evaluate each measurement scale items based on personnel experience on their personnel car.
Table 1: Construct concepts for quality dimensions, customer satisfaction and loyalty with number of
measurement items and reference
Construct
Concept
No. of items References
Performance 8
Brucks et al. (2000), Chen (2007), Chiou et al. (2011), Lin et al.
(2013), Kianpour et al. (2014), Yogi (2015)
Features 10 Brucks et al. (2000), Hazen et al.(2016)
Conformance 4 Sinclair et al. (1993), Larson (1994), Curkovic et al. (2000),
Durability 4
Sinclair et al. (1993), Hansen (1999), Curkovic (2000), Sweeney
(2001), Kianpour (2014),Yogi (2015)
Reliability 5
Sinclair et al. (1993), Larson (1994), Hansen et al. (1999),
Curkovic et al. (2000), Sebastian and Tamimi (2002), Chen
(2007), Yogi (2015)
Serviceability 7 Sinclair et al. (1993), Kianpour et al. (2014), Yogi (2015)
Aesthetic 8 Yuen and Chen (2010), Kianpour et al. (2014), Yogi (2015)
Perceived quality 5 Schvaneveldt (2000, 2005), Kianpour et al. (2014)
Social quality 9 Lin et al. (2013), Yogi (2015), Hazen et al. (2016)
Customer
satisfaction and
loyalty
10
Hallowell (1996), Devaraj et al. (2001), Caruana (2002) , Janda
et al. (2002), Roberts et al. (2003), Yang (2004),
Olorunniwo et al. (2006)
Table 2: Measurement scale items for social quality
No. Measurement scale items
Q1 Your car can quietly starts without annoying the surrounding people.
Q2 Your car has little noise to the surrounding people.
Q3 Your car has little noise to the surrounding people during opening and closing the door.
Q4 Your car lights are easy to see by the surrounding people.
Q5 Your car lights do not disturb other cars and pedestrians.
Q6 Your car's exhaust gas is not strong to the surrounding people.
Q7 Few gases emitted from your car, which is good for environment.
Q8 Less fuel leakage from your car, which is good for environment.
Q9 Your car has a lot of safety functions to prevent accidents with other cars and surrounding
people.
31

31. 3.2 Hypotheses
As presented in Table 3, hypotheses H1 until hypothesis H9 are the hypotheses which associated with eight
quality dimensions (H1 until H8) and social quality dimension (H9). Those 9 hypotheses representing that
each quality dimension has a positive effect on customer satisfaction while hypothesis H10 is a hypothesis
that shows a positive effect on customer loyalty. A hypothetical model including the 10 hypotheses are
constructed as shown in Figure 1.
Table 3: Hypotheses H1 until H10
No. Hypothesis
H1 Performance has a positive effect on customer satisfaction.
H2 Features have a positive effect on customer satisfaction.
H3 Conformance has a positive effect on customer satisfaction.
H4 Durability has a positive effect on customer satisfaction.
H5 Reliability has a positive effect on customer satisfaction.
H6 Serviceability has a positive effect on customer satisfaction.
H7 Aesthetics have a positive effect on customer satisfaction.
H8 Perceived quality has a positive effect on customer satisfaction.
H9 Social quality has a positive effect on customer satisfaction.
H10 Customer satisfaction has a positive effect on customer loyalty.
Figure 1: Hypothetical Model
32

32. 3.3 Questionnaire Survey
The questionnaire survey was conducted through the Internet in early December 2016. The respondents
were the customers who purchased a new car from one of the top six Japanese automobile companies, still
using the car for more than 5 years and driving the car at least once a month. For the questionnaire, there is
a section for demographic attributes including type of engine and section for the measurement scale items
for quality dimensions. The number of collected samples was 1002. After the survey, numbers of
inappropriate reply data were removed, and then the number of valid response samples was 955 (n = 955),
which are the total useable sample size for this research.
4. Hypothetical Models
In this section, we verified the hypothetical model by performing covariance structure analysis.
4.1 Reliability Analysis
Reliability analysis is a method to verify the validity of the items in a certain construct. The high reliable
item and scale are considered to have internal consistency of the observed variables of the construct.
Cronbach α coefficients are a well-known index to measure the internal consistency. As shown in Table 4,
the Cronbach α coefficients for all constructs are more than 0.7 (for example, Cronbach α coefficients for
performance is 0.916), thus items in each construct are likely appropriate to measure the construct.
Table 3: Cronbach α coefficient for all constructs
Construct Concept Cronbach α coefficient
Performance 0.916
Features 0.932
Conformance 0.741
Durability 0.920
Reliability 0.944
Serviceability 0.930
Aesthetic 0.930
Perceived quality 0.909
Social quality 0.943
Customer satisfaction 0.937
Customer loyalty 0.928
4.2 Exploratory Factor Analysis (EFA)
Items were selected by using results of the exploratory factor analysis with the principal axis factoring and
the promax rotation. Particularly, the items of which factor loadings are less than 0.4 are deleted, whereby
the data set in which the factor loading of all items is equal or more than 0.4 is used to verify the hypothetical
model. As a result, several items were deleted (performance = 1 item, features = 3 items, conformance = 2
items, serviceability = 1 item, perceived quality = 1 item and customer loyalty = 1 item).
4.3 Estimating the Models and Verifying the Hypotheses
The hypothetical model was verified by using covariance structure analysis. The paths at the significance
level 5% and standardized coefficient values for the estimated model were shown in Figure 2. The results
presented that the features, conformance, durability, aesthetic and perceived quality impact customer
satisfaction. Each hypothesis is discussed in detail as follows.
33

33. Note 1: Goodness fit indices: GFI: 0.805, AGFI: 0.777, CFI: 0.921, RMSEA: 0.066
Note 2: The solid line represents that the corresponding hypothesis is supported: The path coefficient is
significant at 5% level.
Note 3: The value represents the standardized coefficient at 5% significant level.
Figure 2: Hypothetical model and results
H1: Performance has a positive effect on customer satisfaction.
Since the path from performance to customer satisfaction was not significant at 5% level, the hypothesis 1
(H1) was not supported. The conventional research (e.g., Karnes et al. (1995)) also presented that the
performance was not a very important factor. Since the performance is slightly different between each car
model/brand in Japan, thus performance can be considered as must-be-quality from the viewpoint of Kano
model.
H2: Features have a positive effect on customer satisfaction.
Since the path from features to customer satisfaction was significant at 5% level, the hypothesis 2 (H2) was
supported. The estimated standardized coefficient value was 0.260, which has a relatively strong positive
influence. The conventional researches (e.g., Karnes et al. (1995), Brucks et al., (2000)) also show that
features were highly effected customers. Therefore, it is anticipated that features may attract customers.
H3: Conformance has a positive effect on customer satisfaction.
Since the path from conformance to customer satisfaction was significant at the 5% level, the hypothesis 3
(H3) was supported. However, it was the lowest estimated value among the significant paths, which means
that the effect on customer satisfaction was relatively low. The possible reason might be that most
consumers were not familiar with the concept of conformance.
34

34. H4: Durability has a positive effect on customer satisfaction.
Since the path from durability to customer satisfaction was significant at 5% level, the hypothesis 4 (H4)
was supported. The estimated standardized coefficient value was 0.117, which was also a relatively low.
This result might be due to the fact that every car has high durability and there were not much significant
differences among Japanese cars.
H5: Reliability has a positive effect on customer satisfaction.
Since the path from reliability to customer satisfaction was not significant at 5% level, the hypothesis 5
(H5) was not supported. According to Karnes et al. (1995), although reliability and durability were
originally a different constructed concept, based on factor analysis result, some items can be included in
reliability and durability. This result suggested that the reliability and durability were not discriminant from
the customer perspectives.
H6: Serviceability has a positive effect on customer satisfaction.
Since the path from serviceability to customer satisfaction was not significant at 5% level, the hypothesis
6 (H6) was not supported. Conventional researches suggested that the serviceability has less influence in
purchasing. The results showed that there was not much effect of serviceability on customer satisfaction
even after the customers purchased a product.
H7: Aesthetics have a positive effect on customer satisfaction.
Since the path from aesthetic to customer satisfaction was significant at 5% level, the hypothesis 7 (H7)
was supported. The estimated standardized coefficient value was 0.274, which has a very strong positive
influence on customer satisfaction. Conventional researches (e.g., Karnes et al. (1995)) also suggested that
the aesthetic was very important in product quality. Therefore, it is anticipated that aesthetics may attract
customers.
H8: Perceived quality has a positive effect on customer satisfaction.
Since the path from perceived quality to customer satisfaction was significant at 5 % level, the hypothesis
8 (H8) was supported. The estimated standardized coefficient value was 0.249, which has a relatively strong
positive influence on customer satisfaction. The importance of quality image indirectly and secondarily
recognized such as perceived quality. According to conventional researches (e.g., Garvin (1987), Karnes et
al. (1995)), there were many advertisement media through internet with various types of corporate
advertisements. Besides, the development of Social Network Service (SNS) enables customers to
disseminate individual opinions freely, which can spread rapidly to all over the world. That is why, the
power of "word of mouth" becomes stronger and the importance of perceived quality increases.
H9: Social quality has a positive effect on customer satisfaction.
Although the path from social quality to customer satisfaction was significance at 5% level, the hypothesis
9 (H9) was not supported because the estimated value was negative, which was -0.102. In current
circumstances, consumers themselves seem to recognize social quality as less attractive, as a quality
dimension. Consumers tend to perceive that products with social quality were costly, which was considered
to be expensive for them and likely to have a negative impact on customer satisfaction. From the summary
statistics of purchase intention items concerning social quality, it was found that about 47% of the
respondents consider high fuel efficiency with low fuel consumption was important criteria during product
decision-making. In analyzing the reasons from the free answer section, it turns out that there were various
responses from the viewpoint of their own profit, not from that of society nor third party's interest, that
“fuel economy is good” is equal with “fuel cost reduction”. As a result, customers tend to prioritize their
own interests rather than the benefits to society and third parties.
35

35. H10: Customer satisfaction has a positive effect on customer loyalty.
Since the path from customer satisfaction to customer loyalty was significant at 5% level, the hypothesis
10 (H10) was supported. The estimated standardized coefficient value was 0.629, which has a strong
positive influence on customer loyalty. In the existing customer satisfaction model (e.g., ACSI model), the
relationship between customer satisfaction and customer loyalty was anticipated. Therefore, by increasing
customer satisfaction, customer loyalty might be also increase, which leads to the increase in their purchase
and recommendation intentions.
4.4 Analysis based on Engine types
New Vehicle Intender Study SM (NVIS), which was conducted by JD Power Co., Ltd. in 2016 showed that
the proportion of eco cars listed in the next purchase was increased compared to the previous survey. For
the engine type of a car, a hybrid engine was 53%, which was 5% higher than the previous year. Similar to
‘plug-in’ hybrid car, the utilization rate was also increased by 5% to 18%. In addition, ‘electric vehicle
(EV)’ car increased by 3% from 9% in the previous year to 12%, ‘fuel cell vehicle (FCV)’ was reduced 7%
to 2%. On the other hand, ‘gasoline’ and ‘diesel’ were 65% and 19% respectively, remained unchanged
from the previous year. Therefore, customers who interested in eco cars were also interested in protecting
the environment, which implied that customers’ interest in social quality may also be high. Hence, the
following hypothesis is created and examined.
H11: The positive influence of social quality on customer satisfaction for hybrid car owners is greater
than that for gasoline car owners.
We verified the hypothesis 11 (H11) by using the two-group covariance structure analysis, where the path
from the latent variable (construct concept) to the observed variable (items) is constrained to be same
between the groups. The estimate results were shown in Figure 3. Based on Figure 3, the standardized
coefficient value from the social quality to customer satisfaction for gasoline type engine was -0.104, at a
significant level of 5%. For a hybrid type engine, the standardized coefficient value from social quality to
customer satisfaction was 0.226 (at p value of 0.067). It implied that the hybrid engine car owners
considered the environmental protection and performance as well as the quietness of hybrid engine cars.
(a) Estimated model for gasoline car owners (b) Estimated model for hybrid car owners
(The total samples for gasoline car owners is 876.) (The total samples for hybrid car owners is 66.)
Note：Goodness Indices for : GFI: 0.765, AGFI: 0.731, CFI: 0.904, RMSEA: 0.052
Figure 3: Estimated models based on engine type
36

36. 5. Conclusion
Garvin’s eight quality dimensions and social quality dimension were considered, and on the basis of the
development of measurement scale items and the questionnaire survey data, the effect of these quality
dimensions on customer satisfaction was examined. The results showed that social quality had a negative
effect on customer satisfaction. However, in the case of focusing on hybrid engine car, it implied that social
quality has a positive effect on customer satisfaction.
Therefore, this research revealed the important of social quality (with consideration of society and third
parties) on product/services and therefore another survey need to be conducted to further understand
customer perspectives on social quality.
Acknowledgement
This research was supported by Grant-in-Aid JP 16K03939.
References
1. Brucks, M., Zeithaml, V.A., Naylor, G. (2000): "Price and brand name as indicators of 1uality
dimensions for consumer durables," Journal of the Academy of Marketing Science, Vol. 28, No. 3,
359-374.
2. Caruana, A. (2002): "Service loyalty," European Journal of Marketing, Vol. 36, Issue 7/8, 811-828.
3. Chen, S.C. (2007): "Process performance estimation on the quality characteristics of auto engines,"
International Journal Advance Manufacturing Technology, Vol. 32, 492-499.
4. Chiou, T.Y., Chan, H.K., Lettice, F., Chung, S.H. (2011): "The influence of greening the suppliers
and green innovation on environmental performance and competitive advantage in Taiwan,"
Transportation Research Part E 47, 822-836.
5. Curkovic, S., Vickery S.K., Droge C. (2000): "An empirical analysis of the competitive dimensions
of quality performance in the automotive supply industry," International Journal of Operations &
Production Management, Vol. 20, No. 3, 386-403.
6. Devaraj, S., Matta, K.F., Conlon, E. (2001): "Product and service quality: The antecedent of
customer loyalty in automotive industry," Production and Operations Management, Vol. 10, Issue 4,
424–439
7. Garvin, D.A. (1987): "Competing on the eight dimensions of quality," Harvard Business Review,
Vol. 65 No.6, 101-109.
8. Hansen, E., Bush, R.J. (1999): "Understanding Customer Quality Requirements," Industrial
Marketing Management, Vol. 28, 119-130.
9. Hazen, B.T., Boone, C.A., Wang ,Y., Khor K.S. (2016): "Perceived quality of remanufactured
products: construct and measure development," Journal of Cleaner Production,
http://dx.doi.org/10.1016/j.jclepro.2016.05.099.
10. Hallowell, R. (1996): "The relationships of customer satisfaction, customer loyalty, and
profitability: an empirical study," International Journal of Service Industry Management, Vol. 7,
Issue 4, 27-42.
11. Janda, S., Trocchia, P.J., Gwinner, K.P. (2002): "Consumer perceptions of Internet retail service
quality," International of Service Industry Management, Vol. 13, Issue 5, 412-431.
37

37. 12. Juran, J.M. (2004): Architect of quality: the autobiography of Dr. Joseph M. Juran, McGraw Hill
Professional.
13. JSQC Standard Committee Edition (2011): “Nippon No Hinshitu Wo Ronjirutmeno Hinshitukanri
Yougo Jiten (Terms of quality management for discussing quality in Japan) Part 2” Japan
Standards Association, Tokyo, 88-89
14. Kano, N. (2004) "Quality" in T. Yoshizawa (ed.) Kuoriti Manejimento Yougo Jiten (Glossary of
terms used in quality management),” Japan Standards Association, Tokyo, 580-584. (in Japanese).
15. Karnes, C.L., Sridharan, S.V., Kanet, J.J. (1995): “Measuring quality from the consumer’s
perspective: a methodology and its application,” International Journal of Production Economics,
Vol. 39, No3, 215-225.
16. Kianpour, K., Jusoh, A., Asghari, M. (2014): "Environmentally friendly as a new dimension of
product quality," International Journal of Quality & Reliability Management, Vol. 31 Issue 5, 547 -
565.
17. Larson, P.D. (1994): "Buyer-Supplier co-operation product quality and total costs," International
Journal of Physical Distribution & Logistics Management, Vol. 24, No. 6, 4-10.
18. Lin, R.J., Tan, K.H., Geng, Y. (2013): "Market demand, green product innovation, and firm
performance: evidence from Vietnam motorcycle industry," Journal of Cleaner Production, Vol. 40,
101-107.
19. Olorunniwo, F., Hsu, M.K., Udo G.J. (2006): "Service quality, customer satisfaction, and behavioral
intentions in the service factory," Journal of Services Marketing, Vol. 20, Issue 1, 59-72.
20. Roberts, K., Varki, S. and Brodie, R. (2003): "Measuring the quality of relationships in consumer
services: an empirical study," European Journal of Marketing, Vol. 37, Issue: 1/2, 169-196.
21. Schvaneveldt, Shane J. (2000) “Are Your Products Socially Responsible?, ” working paper, Weber
State University, USA.
22. Schvaneveldt, S.J. (2005): “The Evolving Concept of Quality: A New Framework for Understanding
and Defining Quality,” International Conference on Quality ’05, September 13-16, 2005, Tokyo.
23. Schvaneveldt, S.J. (2011): “Conceptualizing a Social Dimension of Quality,” Production and
Operations Management Society Annual Conference, April 29 - May 2, 2011, Reno, NV.
24. Sebastian, R., Tamimi, N. (2002): "How product quality dimensions relate to defining quality,"
International Journal of Quality & Reliability Management, Vol. 19, Issue 4, 442-453.
25. Sinclair, S.A., Hansen, B.G., Fern, E.F. (1993): "Industrial Forest Product Quality: An Empirical Test
of Garvin’s Eight Quality Dimensions,” Wood and Fiber Science, Vol. 25, No. 1, 66-76.
26. Sweeney, J.C., Soutar, G.N. (2001): "Consumer perceived value: The development of a multiple item
scale," Journal of Retailing, Vo. 77, 203-220.
27. Reichheld, F.F. (2003): “The one number you need to grow,” Harvard business review, 81(12), 46-
55.
28. Yang, Z., Jun, M., Peterson, R.T. (2004): "Measuring customer perceived online service quality,"
International Journal of Operations & Production Management, Vol. 24, Issue 11, 1149-1174.
29. Yogi, K.S. (2015): "An empirical and fuzzy logic approach to product quality and purchase intention
of customers in two wheelers," Pacific Science Review B: Humanities and Social Sciences 1, 57-69.
38

38. 30. Yokoyama, S., Masuda, H., and Seki, T., (2000), Safety as the Socially Responsible Quality, The
Institute of Electronics, Information and Communication Engineers, Technical Report of IEICE, SSS,
Safety 99 (690), 7-13.
31. Yuen, E.F.T., Chen S.S.L. (2010): "The effect of retail service quality and product on customer
loyalty," Database Marketing & Customer Strategy Management, Vol. 17, Issue 3/4, 222-240.
32. Wilson, A., Zeithaml, V.A., Bitner, M.J., Gremler, D.D. (2012): Services marketing: Integrating
customer focus across the firm, McGraw Hill.
33. JD Power Co., Ltd. (2017-1-6)：“2016 NVIS: New Vehicle Intender Study,”
http://japan.jdpower.com/ja/Press-release/2016%20Japan%20VDS_PressRelease_J/.
34. ACSI Official Website (2017-6): http://www.theacsi.org/．
39

39. Trust - the strongest force in brand growth of Chery
Fuming Zhao,1,* Hualin Liu,2 and Zhengkuo Wu2
1,2
Chery Automobile Co., Ltd., NO.8,Changchun Road. Economic & Technological Development Zone,
Wuhu, Anhui Province, P.R.China, 241006.
Abstract: Chery firstly sold five million Chinese brand cars, cumulative total export1.2
million cars. For fourteen consecutive years, Chery became the biggest company in export car
in china. Behind the development of the brand, Chery made great efforts. This article from the
angle of a little guy, presents the development power of a brand—trust. The taxi driver Mr.
Wang is on behalf of the customers, the father is on behalf of the family members of Chery
and the group leader is on behalf of the Chery colleagues. From three aspects they reflect and
exaggerate the power for Chery brand development—trust.
The story will be started lately when I took a taxi to my home in Wuhu after finishing my
business trip in Beijing.
The driver of the yellow blue Chery E5 is in his forties with dark skin. According to the
occupation certification on the instrument panel, his name is Wang Cunxin. Habitually, I
asked him in a roundabout way as a quality worker, How do you feel about this car, Mr.
Wang?”He answered, “That’s ok and it was all right during the past five or six
years.”However, I deliberately said, “Why some people always consider it is bad?” which
seemed to infuriate him. He told me loudly, “They talk nonsense and few of them ever drive
Chery.”No investigation, no right to speak. For example, if road condition is not satisfying,
which causes the home-made car produces some abnormal sounds we could grumble the car.
But just on the same road with a car from a joint venture or foreign countries, we would like
to curse the road. I laughed and didn’t know how to respond to his fast and humor words. Just
at this moment, he added, “Chery is still my choice if I want to replace the car!”
I was enlightened suddenly and told myself that the positive expression on the face of Mr.
Wang, the 541496 kilometers numbered odometer and the words from the bottom of Mr.
Wang’s heart are just the support, affirmation and trust for the brand of Chery!
This trust makes me think of Jan.13, 2006 along with the building passing away swiftly
40

40. outside car window.
That day saw my lucky entry to Chery Corporation after a serious of strict selection. As a
common “fat kid” with an appearance of mischief, I received welcome from schoolmates’
admiring vision, teachers’ favorable nodding, as well as the congratulatory words for natives
and friends. And for the first time, I became the “good boy of neighbors”.”That day, my
fathers sat beside a dining-table full of rich supper, pat me on the shoulder and said, “Do your
best and don’t disgrace yourself!” Since then, when he introduced me to others, he must add
such a word “My son works for Chery.”
However, there is a gap between ideal and reality. Confronted with the high standard of
assembly line work, strict requirement for engine assembly and a variety of specialized
knowledge that I must need to grasp, I nearly collapsed as a greenhand. Instead, my team
head could solve all the technical matters on production line so that I curiously asked him
why he knew so much. He told me he just wanted to grasp technique well and was for fear
that he could be looked down on. Intensive curiosity drove me to know the story behind:
Chery imported a second-hand engine production line from Britain by costing USD 29.8
million in the initial stage of pioneering. Unfortunately, the staff from UK didn’t cooperate
with us and made the work blocked. Then, Chery Corporation resolved firmly that we should
be independent of others and do the work ourselves. What’s more, before leaving, the
Englishman named Jason cast disdainful expression and showed scornful attitude on my team
head, which motivated him to make a firm reservation. He firmly believed Chery could
manufacture engines without the participations of the British. I was encouraged a lot by the
marvelous ability and self-confidence from the team head. Time flies fast and after ten years, I
have become a R&D quality engineer. My ability to solve the problem of abnormal sound
makes me gain laurel. As to my team head, he has acted as the technical supervisor of Chery’s
international project relying on his perfect mastery of technique.
However, the team head suddenly gave a call early in the morning of July 15. Without
sending his regards to me, he told me in a clear but urgent voice that his passport was placed
in the interlayer of the left trousers pocket and he also wanted me to look after his family
members. He gave me no reason and hang up in the dark and terrified night, which scared
me…
41

41. I didn’t know all the things until next day. Originally, the team head and his colleagues
got trapped in the airport due to the military coup in Turkey. His call was to tell me where his
passport in order to discover his corpse in case he dies accidently and as well as to wipe out
the concerns of his wife. What makes me even shocked was that he didn’t go back home and
flied to Egypt to further fulfill his mission in promoting the brand of Chery around the world
with the Chinese Embassy uttermost assistance. I think, just for this reason, we were praised
by both President Xi in Iran and Premier Li Keqiang in Brazil separately.
When I was proud of my team head, the car has arrived at the building of the housing
estate. I saw my fatheras waiting for me with my daughter in his arms. Just his frequent words
over the past ten years, he told others besides that “This is my son and he works for
Chery.”Thinking back the growing days in Chery over the past decade, I want to say to my
father, “Dad, you didn’t lose face and Chery deserves to be trusted…”
Only with the confidence for Chery, home-made cars and national brands from the
people like Mr. Wang silently supporting Chery, the family members like my father
considering Chery as honor, the staff like the team head digging into technology with great
concentration and serving the country while abandoning family union, can a manufacturing
and sales volume over five billion be achieved. For this reason, more than 47 thousand
employees could more securely, confidently and firmly make a national vehicle brand owned
by Chinese. Thus, Chery could manufacture good vehicles Chinese common people can
afford and even has sold the vehicles to over eighty countries and regions worldwide and
achieved an irreplaceable glory brand regarding its passenger vehicles exports over the past
14 years successively.
In Rio Olympic Games, Chinese women’s volleyball team makes Chinese enjoying glory
relying on their yearning for champion over the past 12 years. Chery, for sure, also makes
Chinese brand enjoying a wide reputation based on the confidence of 1.3 billion Chinese
people!
The reason is that trust is the strongest force in brand growth of Chery!
42