REVIEW OF ECONOMIC PERSPECTIVES – NÁRODOHOSPODÁŘSKÝ OBZOR,
VOL. 14, ISSUE 4, 2014, pp. 329–344, DOI: 10.1515/revecp-2015-0003
Customer Satisfaction, Product Quality and
Performance of Companies1
Petr Suchánek, Jiří Richter, Maria Králová2
Abstract: This paper presents an analysis of quality, customer satisfaction and business
performance in food industry. The main objective of the research is to determine the
influence of quality on customer satisfaction and on business performance and competitiveness.
In particular, this paper answers the following research question: Does the
quality of a product result in a satisfied customer and thereby in a well-performing business?
Customer satisfaction is defined as the satisfaction of the customer with a product
and the business performance as a capability to generate profit. Therefore, satisfaction
was examined by the means of a survey using questionnaires, and the performance was
measured by financial data. We managed to find a correlation between the main factors,
although partial results were due more factors mostly statistically insignificant.
Key words: Customer satisfaction, product quality, performance of a company
JEL Classification: L15, L25
Introduction
The aim of the article is to analyse the relationship between customer satisfaction and
performance of a company. The authors assume that customer satisfaction affects corporate
performance since it is surmised that customers will buy a product from companies
they will be satisfied with, i.e. such products that will meet their expectations (in all
aspects). These customers will not only buy the product again later (in case that their
expectations concerning the satisfaction with the product are met) but will also recommend
other (prospective) customers to buy it.
The aforementioned assumption clearly indicates that customer satisfaction can be observed
from various perspectives and during various time intervals. In the research, the
authors took a long-term perspective to observe performance (a five-year interval was
analysed) in order to detect the influence of diverse variables (concerning customer
satisfaction with a company’s products) on the performance of a company.
1
The article represents a partial outcome of a research called The Influence of Quality on Performance
and Competitiveness of a Company (MUNI/A/0738/2012), carried out by the authors of
this article.
2
Masaryk University, The Faculty of Economics and Administration, the Department of Corporate
Economy, Department of Applied Mathematics and Computer Science, Lipová 41a, 602 00
Brno, e-mail: suchy@econ.muni.cz, jiri.a.richter@gmail.com, kralova@econ.muni.cz.
REVIEW OF ECONOMIC PERSPECTIVES
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The article therefore aims to establish whether customer satisfaction (within the defined
variables) influences performance of a company (within the defined variables). Customer
satisfaction is constructed as satisfaction of the customer with a product as such (general);
quality of a product (as perceived by the customer); with customers’ requirements
of a product; willingness to repurchase the product; recommendation of the product to
other customers; and finally, as a comparison of a product with its competition. The
performance of a company was assessed on the basis of two indicators – ROA and ROE,
since the profitability aspect is crucial when considering corporate performance.
Theoretical Framework
Quality is defined as a zero error rate, i.e. the ability to produce a perfect product on the
first try (Parasuraman et al., 1985). Crosby defines quality as the producer’s ability to
meet expectations (Crosby, 1979 quoted in Parasuraman et al., 1985). This definition of
quality is the core of the definition contained in the ISO 9001 standard (cf. ČSN EN
ISO 9001, 2010). As far as the customer’s (consumer’s) point of view is concerned,
quality can be defined as the quality perceived upon the basis of the consumer’s decision
on the overall excellence or superiority of the product (Zeithaml, 1988). All definitions
mentioned above apply to the quality of a product, which is consistent with the
focus of the research into the foodstuffs industry. Widespread approach of Nenadál’s
team understands quality as a degree to which satisfaction is met, measured by a set if
inherent signs (Nenadál et al., 2002). Customer satisfaction can be defined in different
ways - as a comparison of previously held expectations with perceived product or service
performance (Homburg et al.. 2005, Anderson et al., 1994). Alternatively, customer
satisfaction can be defined and measured as consumer ratings of specific attributes
(Gómez et al., 2004). With respect to the focus of the paper on company’s product quality,
we have defined customer satisfaction as a reflection of this quality. It is an easier
view of satisfaction because sales policy, company’s marketing and price policy were
omitted, although they can be considered as factors affecting customer satisfaction and
business profit (Zeithaml, 2000). So we did not proceed in accordance with satisfaction
- profit chain (satisfaction → share of wallet → revenue profit) (Cooil et al., 2007, cf.
Anderson, Mittal, 2000), but we have focused on the product and its impact on customer
satisfaction, and consequently the profit of the company. Of course, we also understand
the relationship of product quality and customer satisfaction over the long term, i.e. that
should a satisfied customer influence profitability positively (through the purchase of a
quality product), he or she must be attracted and held, which is consistent with findings
of Anderson and Mittal (Anderson, Mittal, 2000) or Cooil (Cooil et all, 2007).
Zamazalová (Zamazalová, 2008) also mentions the key factors that affect customer
satisfaction and which can be used to measure customer satisfaction. These factors are
product (in terms of its quality, availability etc.); price (convenient payment conditions
and others); services; distribution; and image of a product. In cases where the determination
of customer satisfaction is through product quality, with that satisfaction simultaneously
affecting quality, a situation arises, where that certain factor affects quality and
is itself affected by it. This problem does not arise in cases of a dynamic perception of
customer satisfaction and quality itself, it only happens when customer satisfaction and
quality are considered to be a process. Quality (both perceived and technical) affects
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customer satisfaction and companies examine that satisfaction and may react to it by
innovating products to increase customer satisfaction further.
The definitions stated above show that the extent to which customer requirements are
met determines customer satisfaction and the degree of meeting them then becomes the
degree of product quality. Every company should therefore struggle to reach the optimal
level of quality of its products to meet the expectations of their customers. In this paper,
quality is defined to be quality as subjectively perceived by customers. Therefore, the
main factor (measure) of product quality is customer satisfaction itself. To achieve high
customer satisfaction, it is crucial for companies to make a product that fulfils the customer’s
requirements.
By definition, performance of a company is linked to productivity, i.e. to a realised
output of a particular company and it can therefore be calculated and analysed further.
There is a range of approaches to evaluating the performance of a company.
The first numerous group of corporate performance assessment studies used in this
research are those based on argumentatively substantiated use of a particular financial
indicator. Comparative indicators of profitability are frequently used for the given purpose,
which is due to their simplicity and extensive information value. Return on investments
(ROI) used in their research by e.g. Smith, Bracker and Miner (Smith et al.,
1987) or Duchesneau and Gartner (Duchesneau, Gartner, 1990) is the most frequently
used indicator of this kind. Almost identical and used just as frequently in evaluating
corporate performance is the indicator of return on assets (ROA), used in their work by,
e.g. a team of professor Blažek (Blažek, 2008), or in Bernolak’s paper (Bernolak, 1997),
Berman’s studies (Berman, 1999), or Liargovas (Liargovas, 2010). Traditionally, return
on equity (ROE), used for the evaluation of corporate performance by e.g. Richard
(Richard, 2000) or Barney (Barney, 1991), is the third frequently used indicator. Return
on sales (ROS) is another potential comparative indicator, and is used in Kean’s analysis
(Kean et al., 1998). Comparative indicators of profitability are not, however, the only
measures of corporate performance. Gu and Gao (Gu, Gao, 2006) Habib (Habib, 2006)
or Viaene (Viaene et al., 1998) used return on assets (ROA) to evaluate the performance
of a company. Many global studies also use the number of employees of a company and
other indicators (see Orser et al., 2000; Mohr, Spekman, 1994; Robinson, Sexton, 1994;
Srinivasan, Woo, Cooper, 1994; Loscocco, Leicht, 1993; Davidson, 1991; O’Farrel,
1986).
However, when measuring corporate performance, the explicit emphasis on a particular
variable - the variable of time - has been missing so far. Should an objective corporate
performance be examined, a company has to reach and maintain that performance over
a specifically defined period of time. Numerous studies reflect this significant aspect in
evaluating corporate performance. For instance, Denison and Alexander (Denison, Alexander,
1986), Dollinger (Dollinger, 1985), Sexton and Robinson (Sexton, Robinson,
1989) or Smith, Bracker a Miner (Smith et al., 1987) use the trend of total revenues per
entrepreneur. The trend of profitability per employee is used by Miller, Wilson and
Adams (Miller et al., 1988), and the development of earnings per employee in time is
the focus of work of Johannison (Johannison, 1993) or Bade (Bade, 1986).
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Last but not least, it must be mentioned that , many authors use a range of specifically
constructed sets of indicators to evaluate performance of companies, i.e. an artificially
created overall indicator (index), combining the aspect of several partial measures. The
authors using this approach are, among others, e.g. Karnani (Karnani, 1982), Oral (Oral,
1993; Oral et al., 1986).
In this paper, quality is linked to the ability of a product to satisfy requirements of a
customer, with customer satisfaction being determined by transaction-specific and cumulative
concepts. The transaction-specific concept defines customer satisfaction as an
evaluation of a particular purchase opportunity following a prior selection (and purchase,
if made) of a product. The cumulative concept, on the other hand, determines customer
satisfaction as an overall evaluation of a complete purchase and the experience of a
consumer with that particular good or service over a specific time period (Anderson et
al., 1994). Since the research focuses on examining customer satisfaction with a specific
product (the aim of the research is not to examine customer satisfaction with the purchase
as a whole), the paper mainly focuses on the transaction-specific concept.
Methodology
Two questionnaires were used in order to investigate company performance and customer
satisfaction. The first questionnaire focused on examining the quality of a company
(including a collection of financial data that provided a basis for analysing corporate
performance by using comparative indicators), whilst the second one focused on
investigating satisfaction of those customers of the companies that were subject to the
research.
The research samples were set and data were collected during the period January –
February 2013. The method used was a face-to-face survey using printed questionnaires
containing scale and semi-opened questions (for both samples). The financial data were
obtained from compulsorily published financial statements.
As for the content, both questionnaires corresponded with each other so that potential
deviations in the results of both pieces of research could be analysed. However, this
paper only deals with the financial results of the companies which are linked to qualitative
characteristics of evaluating customer satisfaction.
Due to the fact that the source financial data are often significantly affected by the environment
development, the companies' performance was expressed as a weighted mean
of the last five years (2008-2012) data. This procedure was based on previous research
(Blažek, 2008) and the ability to express a long-term business performance.
Based on their financial indicators, companies were first divided into performing and
non-performing ones. In the research, emphasis was put on those causal relationships
where strong correlations were expected. The statistical significance of the differences
in the variables under this study, categorised according to financial performance, was
also tested.
In the ensuing analyses, financial performance is assessed by ROA and ROE indicators.
Companies where at least one of the financial indicators of ROA and ROE are above
their median will be considered as performing. (There are ten such companies in the
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sample). As for non-performing companies, those companies will be so designated, for
which both financial indicators (ROA and ROE) fall below their median. (There are
eight such companies in the sample).
In addition to considering the effect of individual variables on financial performance
separately when assessing customer satisfaction, the effect was also considered from a
complex perspective. For this purpose, two classification methods – Discriminant Analysis
and Cluster Analysis were used. The goal of both was to categorise the companies
into two groups based on six variables evaluating customer satisfaction. These two
groups were subsequently compared with groups of performing and non-performing
companies as classified according to ROA and ROE. Discrimination analysis (DA) was
more successful as it, among others, enabled a plentiful interpretation of results. Among
others, it enables to establish which of the variables evaluating satisfaction bear information
substantial for discrimination. The conclusions of DA will be further described
in the chapter of Results.
The Research Sample
The food industry is the sector researched since its products often directly engage in
final consumption. Consumers tend to have an overview of a wide range of products,
buy them often and in significant quantities. This makes it relatively easier to determine
customer satisfaction with the offered products. The basic set of food businesses according
to their main activity (principal activity corresponding to CZ-NACE - Subsection 10:
Manufacture of food products, or 11: Manufacture of beverages) and legal form (private
limited company and limited liability company) consists of 4033 enterprises. Since the
research is focused on QM, it was necessary to eliminate those companies that only
resell their products from foreign manufacturing floors or act in this country solely as
commercial representation. CPV classification criteria resulted in the selection of 458
companies. Businesses that do not report independent financial data and businesses
focused on intermediate food products intended for industrial processing rather than for
final consumption were removed from the sample. The questionnaires were filled by
QM management.
The research sample consisted of 18 enterprises in the area of the food industry (specifically,
according to CZ-NACE classification, group 10 - Manufacturers of food products
and 11 - Manufacturers of beverages). With regard to legal form, 6 of the companies
were limited liability companies and 12 of the sample were publicly listed companies.
The number of employees was the basic criterion of assessing the size of the manufacturing
companies, and in the study, 4 of companies were small (with up to 50 employees),
11 were middle-sized companies (50-249 employees) and 3 were large companies
(over 250 employees).
The second study sample consisted of the customers of the aforementioned companies
with the only criterion of choosing respondents based on their familiarity with the products
of those companies. The questionnaires were distributed to customers through
students of the university, which led to a significant share of young people among respondents.
We received a total of 13,683 correctly and fully completed questionnaires
which were further processed.
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334
Male (50.91%) and female respondents (49.09%) were represented proportionately. The
age group of 16-25 year olds was dominant (62.31%), and was followed, as expected,
by other age groups in ascending order in terms of age, i.e. 26-35 year olds (12.14%),
36-45 (9.87%), 46-55 (9.42%) and the age group of over 55 (4.89%). The age group of
10-15 year olds had only a minor representation (1.37%), which suits the research purpose
as these respondents, as a rule, do not manifest characteristic purchasing behaviour
and cannot select adequately among various products. In terms of profession, students
are the most numerous group (60.07%), followed by employees in the private (17.62%)
and public (10.31%) sectors. Approximately 12% of the sample was made up of less
represented groups of entrepreneurs (5.72%), the unemployed (2.43%) and pensioners
(3.84%).
The basic sample (latest data from year 2012) consists of 49.11% males and 50.89%
females. The age group of 16-25 years old represents just 17.89% of Czech population
and the group of students 13.75%. Therefore, it is appropriate to present the research
findings as satisfaction of dominant group of students aged 16-25 years.
Results
This chapter presents the results of the research in customer satisfaction with products
of selected food companies and the performance of the companies. With regard to the
tools used and construction of questions used in the questionnaire, we only used results
of scale questions. The variables examined were:
• Requirements – these state to what extent the respondents (customers) were satisfied
with the fulfilment of requirements that they had expected of a product;
• Evaluation – states to what extent the respondents (customers) were satisfied with
the quality (technical level) of a product;
• Satisfaction – states to what extent the respondents (customers) were satisfied with a
provided product;
• Repeated purchase – states whether the respondents (customers) would buy the
product again;
• Recommendation – states whether respondents (customers) would recommend other
people to buy the product;
• Comparison – states how the product is doing in comparison with comparable products
on the market.
Table 1 Characteristics of Evaluation of Individual Variables of Customer Satisfaction for
the Companies Examined
Variable ValidN Mean Minimum Maximum Variance Std.Dev.
Requirements 18 7.4133 8.4444 8.4353 0.2053 0.4531
Evaluation 18 7.2385 6.6977 7.7212 0.0919 0.3032
Satisfaction 18 7.5781 6.8888 8.3469 0.1321 0.3635
Repeated Purchase 18 7.4956 6.3333 8.6394 0.2712 0.5208
Recommendation 18 7.4414 5.8888 8.2857 0.2913 0.5397
Comparison 18 6.8353 6.2248 7.6694 0.1905 0.4365
Source: Own calculations.
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First, we determined the average values (of the above stated variables) of the responses
(evaluations) provided by respondents (customers) for all companies (products). The
results are shown in table 1.
The results clearly show that a typical answer of respondents (customers) to all the
stated questions is around the value of 7. This means that the respondents were very
satisfied with the degree of a product’s compliance with requirements (on the scale
value 1 represented the minimum and the value of 10 the maximum compliance with
customer requirements). The variable of Recommendation manifested the highest variability,
while Evaluation showed the lowest. The highest value was reached for Repeated
purchase while Recommendation scored the lowest. Comparison reached the lowest
mean.
As a next step we examined whether there were statistically significant differences in
respondents’ (customers’) evaluations when evaluating the performance of companies
according to ROA and ROE. Table 2 shows the results of two-sample t-tests.
The results show that differences in respondents’ (customers’) evaluations of individual
questions for performing and non-performing companies are not particularly distinctive.
It is therefore not surprising that these results are not statistically significant either. Thus
on their own individual questions cannot differentiate between performing and nonperforming
companies (with respect to ROA and ROE). In follow-up analyses we try to
assess their combined influence when distinguishing performing from non-performing
companies.
Table 2 Statistical Evaluation of Results of Customer Satisfaction Evaluation According to
Individual Variables and Company’s Performance - Performing (Cluster 1) and NonPerforming
(Cluster 0)
Group 1: 1 High performance businesses based on ROA and ROE
Group 2: 0 Low performance businesses based on ROA and ROE
Variable Mean 1 Mean 0 t-value df p
Valid
N 1
Valid
N 0
Std
Dev 1
Std
Dev 0
F-ratio
Varian-
ces
p Vari-
ances
Require-
ments
7.4633 7.3508 0.5120 16 0.6156 10 8 0.5220 0.3743 1.9452 0.3924
Evaluation 7.1979 7.2891 -0.6225 16 5.423 10 8 0.3235 0.2888 1.2553 0.7815
Satisfaction 7.6180 7.5283 0.5082 16 6.182 10 8 0.3987 0.3338 1.4261 0.6542
Repeated
purchase
7.5191 7.4662 0.2081 16 0.8377 10 8 0.6025 0.4361 1.9087 0.4062
Recommen-
dation
7.4082 7.4828 -0.2832 16 0.7805 10 8 0.6440 0.4132 2.4294 0.2549
Comparison 6.7918 6.8897 -0.4613 16 0.6507 10 8 0.3627 0.5362 2.1845 0.2727
Source: Own calculations.
The next step consisted in establishing whether corporate performance (according to
ROA and ROE) correlates with the evaluation of customer satisfaction as a whole, i.e.
on the basis of evaluations of all the six above stated characteristics. In this research,
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336
companies were first divided into two clusters – those where respondents (customers)
are satisfied, and those where they are dissatisfied (based on the six variables). Subsequently,
these clusters were compared to clusters which we gained in sorting companies
according to ROA and ROE so that we could assess to what extent these two clusters
overlap.
In order to cluster companies, a k-mean method was used, where the target number of
clusters was set to the value of 2, and the clustering algorithm started on the 2 first observations.
Alternative algorithm with constant distances provided the same results. The
two clusters which have arisen can be graphically interpreted by the means of six research
questions (see figure 1).
The red cluster in figure 1 can be considered as a cluster of better companies (as seen
from the perspective of customer evaluations) since higher values for the six research
questions (Requirements … Comparison) are considered desirable. When comparing
results received for the clusters based on evaluating customer satisfaction with those of
evaluating companies according to ROA and ROE (i.e. their performance level), we
have discovered a 61% agreement. This means that in 61% of cases, sorting companies
into performing and non-performing according to ROA and ROE brings the same results
as when sorting companies by the six questions evaluating respondent (customer)
satisfaction. We can therefore assume (although not in terms of statistical significance)
that both groups of variables correlate with each other, i.e. customer satisfaction is
linked to company performance. The differences detected by cluster analysis served as a
motivator for us to use discrimination analysis (as a more precise tool) to investigate the
same task as cluster analysis. We assume (due to higher precision of the tool) that the
results will be more conclusive.
The last step consists in applying discrimination analysis which will examine whether
the studied variables affecting customer satisfaction (variables of Requirements …
Comparison) have sufficient power to sort companies into performing and nonperforming
by using the financial indicators of ROA and ROE. This is therefore a more
exact method than the clustering described above as it offers exact tests providing, e.g.,
p value. The method also enables to establish which of the analysed variables can best
sort companies, i.e., which are the most substantial for discrimination.
The six variables related to customer satisfaction were subject to discrimination analysis
carried out in order to establish whether they were able to divide companies into two
groups; to a significant extent similar to the groups determined by ROA and ROE. That
would indicate that the six variables can distinguish between performing and non- performing
companies – from the perspective of customer satisfaction. Dividing companies
into two groups was subsequently used to form one new canonical variable that is a
linear combination of the original six. Thus instead of six, only one value (the so-called
canonical score) will be used for each company. Should this value be higher than the
defined border value, a company will be placed into the “right” group (among performing
companies – according to customer satisfaction), and should the value be lower,
then the company will be placed into the “left” group (among non- performing companies
– according to customer satisfaction). At the same time, the coefficients for a
standardized canonical variable determining the significance of the examined variables
of customer satisfaction evaluation can be ascertained (see table 3).
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Figure 1 Division of Companies Into Performing (Cluster 1) and Non-Performing (Cluster
0) According to Variables Affecting Customer Satisfaction
Source: Own calculations.
Table 3 shows that variables of Satisfaction and Recommendation are of the highest
absolute significance. These two variables (within the sample of companies) thus affect
the value of the score most and consequently also the sorting of companies.
The statistical significance of the canonical score of the given variables can be tested
further when sorting companies into performing and non- performing. The test is assumed
to show multidimensional normality, which can be accepted (bar minor reservations).
Even though the test did not prove significance of the canonical score (the value
of p of 0.258 is not lower than 5%, or 10% respectively of significance level), it is
smaller than values of p shown in table 2. Provided the examined variables are considered
as a single unit (and not examined separately), they seem to be distinguishing between
performing and non- performing companies better. Despite the p value not
emerging as significant, we can expect that from a larger data sample in the future it
will be (see figure 2).
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Table 3 Standardised Canonical Variable Coefficients
Root 1
Requirements 0.4866
Evaluation -0.4281
Satisfaction 3.2849
Repeated Purchase 0.2635
Recommendation -2.3068
Comparison -1.5460
Eigen val 0.8131
Cum. Prop 1.0000
Source: Own calculations.
Figure 2 Scores of Companies Split Into Performing (on the Right-Hand Side of the Boundary
Value) and Non-Performing (on the Left-Hand Side of the Boundary Value)
Source: Own calculations.
Figure 2 shows the scores of individual companies where the boundary value of -0.095
separates two groups of companies. The group of companies on the left-hand side of the
borderline (the blue circles) are, apart from exceptions (the two companies whose red
squares overlap) non-performing companies. With one exception, the companies on the
right-hand side of the borderline (red squares) are performing companies. The “left”
group (sorted according to Requirements … Comparison evaluating customer satisfac-
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tion) is thus almost identical with the group of non-performing companies (sorted by
ROA and ROE). Similarly, the group to the right is almost identical with the group of
performing companies. Only 3 out of 18 companies were misclassified, meaning that 83%
of the companies were sorted identically by customer satisfaction as well as indicators
of ROA and ROE. This can be considered to be an excellent (even though, due to low
number of companies, not statistically significant) result.
Discussion
The research shows that respondents (customers) evaluated companies highly (above
average) in all parameters studied. The factor of Repeat purchase, i.e. the will to buy a
product again, was rated highest by the respondents. This is probably linked with the
high rating of the Satisfaction variable (reaching the highest average score).
On the other hand, the factor of Recommendation scored the lowest value, which might
reflect the general aversion of people to recommend any kind of product to other customers
(in case of satisfaction with a product) or there may be a link with Comparison,
which reached the lowest levels on average. Despite the positive evaluation of the products
of companies under this study, respondents (customers) perceive that these products
provide room for improvement in various aspects in comparison with competitors’
products. The second worst average scoring of Evaluation may be linked with it.
The results show that the aspects of evaluating customer satisfaction individually do not
have any significant impact on the performance of a company. The six abovementioned
quantities combined, however, seem to affect the performance of a company. Based on
this we can conclude that customer satisfaction can be understood in a broader sense as
a sum of several factors; here, this is a combination of general customer satisfaction
with a product, satisfaction with meeting customer requirements of a product, satisfaction
with the technical parameters – with quality. These partial aspects are apparently
reflected in the future purchase of the product and with recommending other customers
to buy that product. Last but not least, the degree of customer satisfaction can be
demonstrated by comparing the product to that of a competitor.
From a long-term perspective, it is apparent that the first three factors (directly linked to
customer satisfaction) impact the immediate performance of a company while Repeat
purchase and Recommendation affect future performance of a company. Comparison of
product with competition serves as a measure of performance because it determines the
gap in performance, indicating room for customer satisfaction improvement with a
perspective to increase corporate performance.
We believe that these inferences correspond to the findings that the most important
factors assessing customer satisfaction with the capacity of dividing companies into
well-performing and non-performing are simply those of Satisfaction and Recommendation.
In the long run immediate performance (linked to general satisfaction of customers
with a product) and future performance (linked to a recommendation to buy a product)
unite.
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340
Conclusion
As far as the statistical significance of the results is concerned, we cannot regard the
connection between customer satisfaction and company performance as proven. However,
the results suggest that there indeed is a correlation. More extensive research (in
terms of the number of companies in sample) will have to be conducted to verify this.
We assume that despite the statistical insignificance of the results, this paper brings
interesting findings. Customer satisfaction does not seem to be a simple (basic) variable;
on the contrary, it is composed of several partial variables that affect the company and
its performance in a complex manner. Only in this way is the power of the effect sufficient
to influence corporate performance.
At the same time, the variables related to customer satisfaction seem to be exerting
influence at various time intervals, so the complexity of their effect can best be monitored
over a long period of time. For companies this is an important finding since, if
they intend to maintain long-term performance, they have to watch not only immediate
customer satisfaction but also ensure that customers are satisfied in the future as well.
The companies ensure such satisfaction, among others, by the quality of their products,
and they logically have to continuously improve that quality (based on the requirements
of customers).
Further research should thus examine the influence of the abovementioned factors on
company performance as well as mutual relationships and links of individual factors so
that it is possible to create a compact unit and a complex model comprising product
quality, customer satisfaction and corporate performance. The range of factors might be
further extended by price, for example, which affects customer satisfaction on the one
hand and company performance on the other, and which is one of the most important
factors influencing customer decision-making when buying a product (at least in the
Czech Republic).
It is obvious that the characteristics tested are not the only customer satisfaction characteristics,
i.e. that there are other factors that influence the satisfaction. With respect to
studies by Cooil (Cooil et al, 2007) it can be assumed that these are demographic characteristics
(age, income, education) and situational characteristics (expertise and length
of relationship). It appears that it is no longer sufficient to have a product tailored just to
the needs of the customer (in the technical meaning), but even price and customer service
should be taken into consideration (in addition to product quality itself) (Gómez et
all, 2004).
It seems that it is necessary to act comprehensively (on a much broader scale) and strategically
(long-term) on the customer satisfaction. It is also possible to choose different
strategies, i.e. different mix of marketing tools, although the firm must consider the
specific impact of this strategy on firm performance (compare to Gómez et al., 2004).
To achieve higher customer satisfaction, it is necessary to determine the optimal mix of
marketing tools (i.e. not just the product) and not only with respect to competition, but
in particular with regard to the character, preferences and options of the customer. This
is not possible without sophisticated research of customer customs, values and preferences.
Many Czech businesses are not aware of it and constantly underestimate these
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facts (Suchánek et. al, 2013). That is what the cause may be of a different perspective
on product quality by the business and its customer.
Research Limitations
As far as statistical significance of the results is concerned, no link between customer
satisfaction and business performance was proven. On the other hand, the results suggest
that such a correlation does exist. Nevertheless, it would be necessary to conduct a
broader research in which the sample of companies examined would be increased. Despite
the statistical insignificance of the results, the article offers interesting findings: It
seems that customer satisfaction is not a simple (basic) variable, but rather a composition
of several sub-variables arising from a particular company and its performance.
Only then is the effect of customer satisfaction strong enough to affect the corporate
performance.
As concluded by the findings, conclusions and current researches, the concept of customer
satisfaction must be broader and not focused just on the product quality: significant
factors influencing satisfaction are omitted and missing in the final models. The
product quality has to be better defined with respect to the factors mentioned (price,
customer service, specifications). It is also necessary to formulate the questionnaires in
order to avoid different understanding of concepts such as satisfaction, quality, expectations,
etc. Finally, it was not possible to obtain data from more businesses, which, however,
was corrected in subsequent research. In a similar way, it is appropriate to try to
get a representative data from consumers. Although the findings are not statistically
significant, it can be assumed that the findings will be proved with research which is
currently being carried out.
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