QUALITY AND PERFORMANCE OF THE COMPANY
Milan Sedláček
Petr Suchánek
Jiří Špalek,
Faculty of Economics and administration, Masaryk University,
Lipová 41a, Brno, 602 00, Czech Republic
100029@mail.muni.cz
suchy@econ.muni.cz
spalek@econ.muni.cz
Keywords: quality, performance, financial analysis, cluster analysis, customer
satisfaction
ABSTRACT
The aim of this article is to find out the level of product quality in a company and
identify those factors of quality that affect a company’s performance. Research has
been carried out in companies in the Czech Republic because there are no specialized
studies of a similar kind in the Czech Republic which would examine the relationship
between quality and performance. Another aim of the research, some results of which
are presented in this paper, is to contribute to filling the gap.
INTRODUCTION
A number of authors deal with quality and business performance of a company;
however, their interpretation of quality in terms of a company is very comprehensive
(for example Kaplan and Norton, 1992 or Madu, Kuei, Jacob, 1996). Other authors
analyze quality of a company through customer satisfaction, but they do not provide
any quality concept within a company despite the fact that the connection with a
performance is rather loose in this case (for example Parasuraman, Zeithaml, Berry,
1988). Our research focuses on examining product quality and customer satisfaction
in a company product. We associate the observed level of quality with a performance
of a company. This enables us to identify those quality factors, which directly affect a
company’s performance.
The aim of this article is to find the level of product quality in a company and to
identify those quality factors that affect a company’s performance. The sample data
we use come from Czech companies. Specialized studies of a similar kind which
would examine the relationship between quality and performance are very rare in the
Czech Republic; therefore our research aims to contribute to filling the gap.
The presented analysis is an output of ongoing research of the research team. Previous
results illustrated that most companies in the Czech Republic consider the quality of
their products or services as superior. At the same time, these companies are aware of
the positive impact of the superior quality of their products on the efficiency of their
company (see Blažek et al., 2009, for further information). However, these
conclusions do not provide evidence concerning other aspects of company
performances which could reflect even quality management.
THEORETICAL FRAMEWORK
Company performance can be measured in many different ways. A usual approach is
to evaluate the performance by financial ratios such as return on investment
(Duchesneau and Gartner 1990; Smith, Bracker and Miner 1987), return on sales
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(Kean et al. 1998) or return on equity (Richard 2000; Barney 1991). In case of a new
company without a profit history we can use the current amount of revenues or more
commonly the number of employees (Orser, Hogarth, Riding 2000; Mohr, Spekman
1994; Robinson, Sexton 1994; Srinavasan, Woo, Cooper 1994; Loscocco, Leicht 1993;
Davidson 1991; O’Farrel 1986). Moreover, there are other opportunities how to
evaluate company performance: dynamic variables such as improvement in ROI
across time (Miller, Wilson, Adams 1988), other financial ratios like revenues/income
per worker (Johannison 1993; Bade 1986) or liquidity and sales volume (Deng, Dart
1994).
While defining the term quality, it is necessary to note that a single correct definition
of what exactly quality is does not exist. For example, Garvin (Garvin, 1987, Garvin,
1984) defines five basic building blocks of quality together with its eight dimensions,
whose meeting is critical for considering production quality or even quality of a
company itself. When empirically verifying the relationships between the application
of quality management and company performance, we have to take into account the
fact that when looking for causal relationships it is necessary to work with quality
perception and not with its objective operationalization. The reason is customers’
subjective quality assessment as their opinion is the basis for their decision to buy,
which is the basic building block of financial indicators. The best way to increase
company performance is therefore increasing quality, which is a result of a wellrealized
business strategy.
According to Japanese philosophy, quality is a zero defect – doing it right the first
time (Parasuraman, Zeithaml, Berrry, 1985). Crosby defines quality as conformance
to requirements (Crosby, 1979 reference from Parasuraman, Zeithaml, Berrry, 1985).
This concept of quality makes a core of definition of quality according to ISO 9001
(compare with ČSN EN ISO 9001 ed. 2, 2010). Companies operating in industry
perceive quality in accordance with this aforementioned generally accepted definition,
as a degree of meeting requirements by a set of inherent traits.
Existing models of quality assessment are not directly associated with performance, or
they are not directly linked to business performance indicators. An exception in this
respect is Everett, who conducted extensive research with his team on the approaches
to quality improvement including business performance. It was found that the
financial indicator of business performance measured by ROA depends on three
factors: knowledge of quality, senior management involvement and employee
compensation and recognition (Everest et all, 1997).
These factors stem from a generally recognized quality model in which the authors
agree on eight fundamental quality factors: the role of top management leadership, the
role of the quality department, training, product or service design, supplier quality
management, process management, quality data and reporting and employee relations
(Saraph, Benson, Schroeder, 1989). Quality data and reporting is understood as
monitoring costs associated with quality measuring, an information system, and
methods aimed at determining the level of quality; however, the last factor does not
directly include indicators of company performance.
Given the fact that this model focuses only on the quality of management, it was
extended to include product and process factors (rate of product/process change,
degree of manufacturing content, proportion of product/service purchased outsider,
extent of batch vs. continuous process, product complexity) as well as factors related
to the market (the degree of competition, the extent of barriers to entry in the industry,
the extent of quality demands by customers, the extent of government quality
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regulation) (Saraph, Benson, Schroeder 1991). Even here, however, the standard
indicators of business performance are not mentioned.
METHODOLOGY
The research is based on primary data obtained from a survey using a structured
questionnaire. Respondents were asked to enter specific data from a balance sheet and
profit and loss account. Companies were invited to update and complement the data.
The primary purpose of collecting the data was to construct certain indicators
evaluating financial performance of a company. The questionnaire consisted of two
parts. The first – general – part comprised questions relating to monitoring quality and
the relation between quality and competitive ability of a company in addition to the
usually examined corporate characteristics (closer identification and classification of a
company). The second – major – part of the questionnaire consisted of sixteen closed
questions: six of them in the form of a ten-degree scale and the remaining ten
questions mapping selected parameters (characteristics) of quality management of a
company.
The answers collected were processed with basic tools of statistical analysis. This
involved mainly the methods of univariate and bivariate analyses. First, the frequency
of occurrence of individual answers was examined, as well as the mean value answers
of respondents. To be able to articulate and subsequently identify relative correlations
of answers to questions (of the questionnaire), this primary analysis will serve as a
basis for the secondary, bivariate analysis. With respect to a relatively low number of
respondents, the results stated below are mostly based on a variety of contingency
tables, i.e. on identification of varying occurrence of the phenomena controlled for the
groups (clusters) of companies in the research. The figures show percentages of
respondents’ answers for both of the clusters.
To examine the financial situation of companies (i.e. performance), a method of a
financial analysis, specifically a ratio indicator analysis, is to be applied. Indicators
were selected to allow for assessment of all key areas of an enterprise, i.e. profitability,
activity, indebtedness and liquidity, which are the factors that make it possible to
determine a complex financial situation of a company. The construction of the
selected indicators is grounded in the authors’ previous research (see e.g. Suchánek,
Špalek, Sedláček, 2010, for further information).
To divide companies into a high performing group and a low performing group, a
cluster analysis is used. The clustering uses the method of a K-means cluster analysis.
Based on the input financial indicators, companies are divided into two disjunctive
and relatively homogenous groups (clusters). The guideline in this case is a minimum
inter-cluster distance between individual members of a cluster. The selected method is
the minimum distance method. It is derived from the Euclidean metric, i.e. the
minimum sum of squares. The groups (clusters) are thus formed by the companies
which demonstrate the biggest concordance with the selected (financial) indicators.
Since more than one financial indicator is used, the shortest distance is determined by
the shortest scalar distance of vectors of the financial indicators. To guarantee full
comparability of the financial indicators (since their units and relative values differ), it
is necessary to standardize the individual coordinates (indicators) before carrying out
the cluster analysis itself. So called z-scores are used for the standardizations.
To achieve maximum objectivity in dividing individual companies into clusters, a
retrospective progression of data of a financial indicator combination is used. The
analysis is fed with data of a five-year period of 2006 – 2010. Respondents are
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selected at random from the basic sample of 143 573 companies in 2011. The research
sample includes 144 companies mainly from the manufacturing industry.
The resulting groups (clusters) of high performing (cluster A) and low performing
(cluster B) companies are contrasted with the above mentioned characteristics of
quality collected with the questionnaire. We are mainly interested in comparing how
the values correspond to or differ from the mean values of the given indicators with
respect to either different types of answers or corporate characteristics.
CHARACTERISTICS OF THE SAMPLE
As mentioned above, the basic sample contains 143 573 enterprises; subsequently 144
companies from the manufacturing industry were randomly selected from this sample.
In terms of the number of employees, the distribution of businesses was even as it
contained 37.1% of small businesses (up to 49 employees), 30.5% of medium-sized
enterprises (50-249 employees), and again 30.5% of large enterprises (over 250
employees).
In terms of legal form, the sample was restricted to public limited companies and
private limited companies (as these companies are legally obliged to publish their
financial statements in the Commercial Register). The sample included 44.5% of
public and 55.5% of private limited companies, i.e. the representation of both the
types of companies was almost even.
In terms of the existence of a specialized quality control department in a company (or
a specialized employee who would deal with quality) it was found that 64% of
businesses have this department whereas 35.4% of enterprises do not have it (0.6% of
companies did not provide this information). In addition, 76.2% of companies own a
certificate of quality (the most – 65.9% of businesses – own ISO 9001 certificate).
Regarding the location of companies, most of them were located in the South
Moravian Region (36.8% of enterprises), fewer from the Pardubice Region (11.1% of
companies), the Vysocina Region (10.4% of enterprises), Zlín region and MoravianSilesian
Region (both 8.3% of enterprises), Prague and the Olomouc Region (both
7.6% of enterprises) and fewest from the Hradec Kralove Region (4.9% of
companies), the Central Bohemian Region (2.1% of companies), South Bohemia and
the Liberec Region (both 1.4% enterprises). The regions of Plzen, Karlovy Vary and
Usti were not represented with any enterprise.
RESULTS OF THE QUALITY AND PERFORMANCE OF COMPANIES
In this section we present the results of a cluster analysis: based on the regularly used
and constructed ratios (identified from the accounting statements of enterprises) three
clusters of enterprises with a (statistically significant) difference in performance were
created. Subsequently, parameters and quality characteristics (identified in the
questionnaire survey of enterprises) were identified for these clusters. Statistically
significant differences in these parameters and statistics between individual clusters
were primarily looked for and found.
Results of the cluster analysis
Based on the results of a cluster analysis, companies were divided into three groups:
excellent companies (cluster A), average companies (cluster B) and below-average
companies (cluster C). In the end, the companies were divided into the clusters based
on ROA and ROE indicators due to the best results of the cluster analysis (the cluster
analysis was conducted with various combinations of ROA, ROE, asset turnover,
third-degree liquidity and indebtedness indicators). Average values of both the
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indicators in individual years are shown in Table 1 while average values of the other
indicators of business performance are shown in Table 2.
Table 1: Average values of ROA and ROE indicators for individual clusters
Cluster A Cluster B Cluster C
ROA 2006 0.225 0.095 0.014
2007 0.257 0.110 0.004
2008 0.213 0.094 -0.012
2009 0.208 0.069 -0.031
2010 0.218 0.052 0.011
ROE 2006 0.367 0.167 -0.023
2007 0.391 0.184 -0.016
2008 0.341 0.145 -0.060
2009 0.326 0.101 -0.102
2010 0.386 0.083 -0.004
Source: Authors’ calculations
Table 1 shows that the profitability of all the three groups (clusters) of the companies
is significantly different. Cluster A of excellent companies maintains ROA above
20%, although the value of the indicator fluctuated in a rather negative trend in
individual years. The ROA indicator of average companies is significantly lower and
the indicator value fell more significantly with these companies in the researched
years. The value of the ROA indicator with below-average companies fluctuated
around zero, with the fluctuations being more significant than in the two previous
groups of enterprises.
Table 2: Average values of the asset turnover, quota of equity and long-term liquidity
indicators for individual clusters
Cluster A Cluster B Cluster C
Asset turnover 2006 2.539 1.800 1.383
2007 2.436 1.897 1.394
2008 2.446 1.653 1.323
2009 2.468 1.552 1.082
2010 2.342 1.373 1.222
Quota of equity 2006 0.509 0.442 0.553
2007 0.551 0.483 0.572
2008 0.523 0.497 0.597
2009 0.537 0.535 0.591
2010 0.507 0.536 0.589
Long-term liquidity 2006 2.396 2.262 2.827
2007 2.729 2.308 2.521
2008 3.283 2.201 2.577
2009 3.014 2.621 2.406
2010 3.673 2.573 2.536
Source: Authors’ calculations
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Differences in values of the ROE indicator are even more significant in this respect
between the clusters as excellent companies experienced a significant growth in the
indicator in the last researched year compared with average companies. On the
contrary, below-average companies also showed a slight increase in the indicator in
the last year, but in all the years, the values of the indicator were in negative numbers.
Table 2 shows that the differences in the other indicators were not as clear and
unambiguous as in case of ROA and ROE indicators. The most significant differences
exist in the asset turnover, which is significantly higher in the case of excellent
companies in cluster A in all the years than in the other two clusters. It is interesting
that this indicator in cluster A rose in the crisis years of 2008 and 2009 and sharply
decreased in 2007 (when the economy was most efficient in the Czech Republic) as
well as in 2010. In contrast, the other two clusters exhibited a downward trend of the
indicator (from 2007) while in cluster C a slight increase in the indicator repeated in
2010.
The quota of equity, measuring a company’s indebtedness, shows that the
indebtedness of enterprises was in all clusters within the recommended values, i.e.
around 50%. Differences between the clusters are small for this indicator, however,
they exist. For cluster A, indebtedness fluctuated with a negative trend in the years,
and in 2010 it was almost the same as in 2006. Indebtedness of cluster B was slightly
higher than in cluster A, but it continuously decreased in the years (to lower values
than in cluster A). Cluster C was the best in terms of indebtedness, i.e. this indicator
was the highest in the years (it fluctuated with a growth trend).
In the case of long-term liquidity, the differences between the clusters are the smallest.
This indicator is slightly above average for all the three clusters (compared to the
recommended values of 2-2.5). In the case of cluster A, this indicator grew with a
positive trend while in cluster B it declined first (years 2007 and 2008) and after an
increase in 2009 it dropped again in 2010. Cluster C showed fluctuations with a
slightly negative trend.
Table 3: Average values of indicators in manufacturing enterprises in different years
2006 2007 2008 2009 2010
ROE 0.126 0.151 0.109 0.062 0.113
ROA 0.097 0.116 0.088 0.050 0.072
Asset turnover 1.550 1.400 1.390 1.220 1.340
Quota of equity 0.498 0.522 0.521 0.526 0.514
Long-term liquidity 1.450 1.460 1.420 1.540 1.580
Source: authors, based on http://www.mpo.cz/cz/ministr-a-ministerstvo/analyticke-
materialy/#category238
When comparing the clusters with average values of the manufacturing industry for
the indicators (manufacturing industry averages are shown in Table 3), it is possible to
observe the following: cluster A shows highly above-average ROE, ROA and asset
turnover indicators in all the years. Indebtedness of companies in the cluster is around
the average values of the industry and their long-term liquidity is much higher. These
results clearly show that cluster A represents highly efficient enterprises of the
manufacturing industry in terms of their performance.
Cluster B is characterized by ROE and ROA values that are around the sector average
(usually above it) and asset turnover that is slightly above average in the surveyed
years. On the contrary, indebtedness of cluster B enterprises is usually slightly higher
than the industry average. However, long-term liquidity is highly above average,
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although it does not exceed the values of cluster A (with one exception). In terms of
performance these are average-efficient businesses.
Cluster C is characterized by values of ROA and ROE indicators that are considerably
below average (ROE being negative during the whole surveyed period). The asset
turnover indicator is slightly below average, while indebtedness of companies in the
cluster is slightly lower than the sector average. Long-term liquidity is, however,
highly above average. In terms of performance these are below-average companies;
nevertheless, with regard to their indebtedness and liquidity the situation of the
companies may not be as critical as it might seem from the profitability indicators.
Results of the quality analysis of the companies
Individual responses to questions regarding the quality of enterprises were
subsequently compared and statistically evaluated within the created clusters. The
boundary of statistical significance of answers was set to a standard level of 10%. The
following tables and text relate primarily to statistically significant results (if the
results were statistically insignificant, it is explicitly stated by them); however, it is
necessary to admit that there were only 23% of them with regard to the number of
questions in the questionnaire.
Table 4: Product evaluation in terms of quality (response rate in %)
1 2 3 4 5 6 7 8 9 10
Cluster A 0.0 3.4 0.0 3.4 0.0 10.3 13.8 24.1 24.1 20.7
Cluster B 0.0 0.0 0.0 1.2 0.0 4.7 5.8 19.8 31.4 37.2
Cluster C 3.4 3.4 6.9 0.0 0.0 0.0 10.3 31.0 17.2 27.6
Source: Authors’ calculations
Table 4 gives evaluation of the product in terms of quality, and this evaluation was
subjective, i.e. it was conducted by the businesses alone. Evaluations were made with
a scale ranging from 1 (very low quality) to 10 (very high quality). The table shows
that most businesses across the clusters assess their product quality as above-average.
The results, however, differ in a degree of the above-average assessment.
Table 5: Reasons which prompted a company to monitor customer satisfaction
Cluster A Cluster B Cluster C
improving product (service) quality 67.9% 82.9% 60.7%
feedback 82.1% 74.4% 75.0%
effort to retain customers 75.0% 78.0% 71.4%
certification 39.3% 30.5% 35.7%
economic recession (financial crisis) 3.6% 7.3% 7.1%
competition 7.1% 46.3% 25.0%
others 0.0% 1.2% 3.6%
Source: Authors’ calculations
Most businesses of cluster A evaluate the quality of their products with mark 8 and 9
(both 24.1% of enterprises) and the highest mark of 10 (20.7% of companies). Fewer
companies then evaluate the quality of their products with mark 7 (13.8% of
enterprises) and 6 (10.3% of companies). On the contrary, average companies assess
the quality of their products primarily with mark 10 (37.2% of enterprises) and 9
(31.4% of companies), and to a lesser degree with mark 8 (19.8% of companies).
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Enterprises of cluster C most frequently evaluate the quality of their products with
mark 8 (31% of enterprises), 10 (27.6% of enterprises), and to a lesser degree with
mark 9 (17.2% of enterprises) and 7 (10.3% of companies).
Another question focused on whether companies pursue customer satisfaction (results
were not statistically significant). It was found that the majority of companies in the
clusters monitor customer satisfaction (specifically, 96.6% of enterprises in cluster A,
90.7% of enterprises in cluster B, and 96.6% of enterprises in cluster C). The reasons
which prompted the company to monitor customer satisfaction represented another
research factor; the results are summarized in Table 5. The table shows that
companies in the clusters reported different causes that made them monitor customer
satisfaction.
Efficient companies of cluster A reported feedback (82.1% of companies) followed by
efforts to retain customers (75% of companies) and improving product quality (67.9%
of companies) as the most common cause that made them monitor customer
satisfaction. Only to a lesser extent they reported certification (39.3% of companies)
and almost none of the other causes. Average companies of cluster B often cited the
same causes, but in a different order. The most common cause of monitoring customer
satisfaction in these companies was improving product quality (82.9% of companies)
followed by efforts to retain customers (78% of companies), feedback (74.4% of
companies), and to a lesser degree competition (46.3% of enterprises) and
certification (30.5% of companies). For below-average businesses the most common
cause was feedback (75% of companies), an effort to retain customers (71.4% of
companies), improving product quality (60.7% of companies), and to a lesser degree
certification (35.7% of enterprises) and competition (25% of companies).
Table 6: Number of complaints per 100 products according to individual clusters
Number of complaints per 100 products Cluster A Cluster B Cluster C
0-1% 30.8% 57.9% 58.3%
2-3% 38.5% 31.6% 29.2%
4-5% 11.5% 9.2% 12.5%
6-7% 11.5% 0.0% 0.0%
8-10% 3.8% 1.3% 0.0%
25% and more 3.8% 0.0% 0.0%
Source: Authors’ calculations
The authors also investigated whether the companies monitor the number of
complaints in a company (the results were not statistically significant), and it was
found that the majority of the businesses monitor this indicator (specifically, in the
case of cluster A 89.7% of enterprises monitor the number of complaints, in the case
of cluster B it is 89.4% of businesses and in the case of cluster C 82.8% of
enterprises). In case the companies stated that they monitor the number of complaints
in the company, they were further asked about the number of complaints per 100
products. The results are summarized in Table 6.
In complaints per 100 products, there are once again certain disproportions between
clusters. The most efficient companies of cluster A reported the number of complaints
mostly between 2-3% (38.5% of companies), between 0-1% (30.8% of enterprises)
and to a lesser degree between 4-5% and 6-7% (both 11.5% of companies). On the
contrary, average companies most frequently reported the number of complaints
between 0-1% (57.9% of enterprises), 2-3% (31.6% of companies) and to a lesser
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degree between 4-5% (9.2% of companies). Below-average businesses assessed the
situation of complaints similarly, i.e. the most frequent number of complaints was
between 0-1% (58.3% of companies), between 2-3% (29.2% of companies) and to a
lesser degree between 4-5%. It is interesting that a higher number of complaints is
declining as business (cluster) performance is decreasing.
Another parameter surveyed was whether a company systematically controls the
quality of a product in the company. The results are summarized in Table 7 and they
show that the quality is most frequently systematically controlled in the average
companies of cluster B (96.5% of enterprises), to a lesser degree the most efficient
enterprises of cluster A (89.7% of enterprises), and to the least degree below-average
businesses of cluster C (82.8% of companies).
Table 7: The company systematically controls quality
Cluster A Cluster B Cluster C
no 10.3% 3.5% 17.2%
yes 89.7% 96.5% 82.8%
Source: Authors’ calculations
The authors were also interested in which performance indicators the companies
monitor, and whether the businesses associate the indicators with quality (and if so,
which of them). The results are summarized in Table 8. The left side of the table
shows the frequency of individual factors in clusters and the right side shows the
number of individual enterprises that associate these factors with quality within each
of the clusters.
Table 8: Monitored performance indicators and their association with quality
Frequency of factors Frequency of factors in
association with quality
Cluster ACluster BCluster CCluster A Cluster B Cluster C
sales 79.3% 74.4% 65.5% 13.8% 16.3% 31.0%
financial results 48.3% 59.3% 48.3% 41.4% 23.3% 34.5%
costs 51.7% 59.3% 44.8% 27.6% 22.1% 48.3%
use of capacity,
productivity, volume
of production
37.9% 40.7% 34.5% 37.9% 23.3% 41.4%
added value 31.0% 25.6% 27.6% 10.3% 19.8% 34.5%
profitability 17.2% 26.7% 17.2% 17.2% 25.6% 37.9%
liquidity 3.4% 14.0% 10.3% 24.1% 29.1% 44.8%
complaints 55.2% 66.3% 58.6% 3.4% 4.7% 13.8%
employee register 6.9% 22.1% 17.2% 31.0% 32.6% 51.7%
customer satisfaction 82.8% 70.9% 69.0% 10.3% 12.8% 27.6%
Source: Authors’ calculations
Table 8 clearly shows that cluster A businesses use most the qualitative indicator of
customer satisfaction, and only then follows the financial indicator of (absolute) sales,
and a further financial indicators – costs – is preceded by complaints. These are
followed by other financial indicators, i.e. financial results, and to a lesser degree use
of capacity or productivity. The least used indicators include profitability and liquidity.
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The factors most associated with quality are financial results, use of capacity
(productivity), employee register and costs. On the contrary, customer satisfaction and
sales are associated with quality the least.
Cluster B businesses prefer monitoring sales, followed by customer satisfaction,
complaints, financial results, costs and to a lesser degree use of capacity
(productivity). The factors most associated with quality include employee register,
liquidity, profitability, financial results, use of capacity (productivity), and costs. In
this case, the frequency distribution is more even than in cluster A.
In the case of cluster C, the most commonly used performance factors are customer
satisfaction, sales, complaints and to a lesser degree financial results, costs and use of
capacity (productivity). The factors most associated with quality include employee
register, costs, liquidity, use of capacity (productivity), and to a lesser degree
profitability, financial results, and sales.
Table 9: Disadvantages of a company with respect to its competitors
Cluster A Cluster B Cluster C
funding opportunities 22.7% 22.6% 31.8%
company size 59.1% 46.8% 22.7%
costs of operation 27.3% 50.0% 45.5%
range of provided services 59.1% 24.2% 13.6%
location 31.8% 29.0% 18.2%
others 4.5% 6.5% 4.5%
Source: Authors’ calculations
The last statistically significant finding were disadvantages (weaknesses) reported by
the businesses in relation to their competitors. The results are summarized in Table 9.
The results show that excellent companies of cluster A perceive weaknesses mainly in
the company size and the range of provided services (both 59.1% of enterprises), to a
lesser degree their location (31.8% of companies), costs of operation (27.3% of
companies), and funding opportunities (22.7% of companies). On the contrary, the
average businesses see the biggest problems in the costs of operation (50% of
companies), company size (46.8% of companies) and to a lesser degree in their
location (29%), the range of provided services (24.2% of enterprises), and funding
opportunities (22.6% of companies). The below-average enterprises of cluster C also
see the biggest weaknesses in costs of operation (45.5% of companies), funding
opportunities (31.8% of companies), and to a lesser degree in company size (22.7% of
enterprises), location (18.2% of companies), and the range of provided services
(13.6% of companies). It is also clear that the excellent and average enterprises
reported more frequently a higher number of disadvantages than below-average
businesses.
DISCUSSION
The evaluation of product quality offers a rather surprising finding that although the
most efficient companies of cluster A assess their quality as high they do not see it as
the highest. On the contrary, the average businesses received the highest marks for
product quality. Even inefficient firms assess their product quality very high, although
the results are more fragmented here (compared to the remaining two clusters).
Because it was a subjective quality assessment, an explanation can be made that the
companies did not assess the quality of their products objectively enough (in
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particular regarding cluster C), and it can thus be hypothesized that as the level of
business performance declines, the objectivity of product quality evaluation decreases.
Such a hypothesis, however, can be confirmed only in a survey of consumer
satisfaction with the quality of production of the researched enterprises, which the
authors plan to conduct in the second phase of the research on the relationship of
quality and efficiency in the fall of 2012.
Another possible explanation is the lack of communication with customers due to an
incorrectly set marketing mix or even a wrong marketing strategy (or its complete
absence in a company). In this case it would be of course possible that an otherwise
quality product would not make it to a customer at all, or a customer would not learn
about it at all. However, in this respect we could possibly talk about the lack of
quality or low quality of an enterprise as a whole (as understood by Kaplan and
Norton, 1992). It would then be necessary to examine internal processes of a company,
or possibly its marketing strategy including the tools used within the marketing mix,
market segment, which the company focuses its product on, etc.
To ensure high production quality, it seems necessary to monitor customer
satisfaction (it stems not only from the authors’ own research). It is clear, however,
that it is important to determine what made companies monitor this satisfaction. The
most important aspects in this regard (with respect to the performance) include
feedback and an effort to retain customers. Improving product quality is in the third
place in this respect despite the fact that it is closely related to an effort to retain
customers.
It seems that the motives that make companies monitor customer satisfaction are
related to (or anticipate) the way of monitoring customer satisfaction as well as its
further use in a business (especially in improving product quality). It is obvious that
particularly inefficient companies do not fully realize these links. On the other hand,
average businesses seem to realize these links but they rather respond to stimulations
coming from competition, which means that their actions (reactions) come delayed (or
late).
It is possible to hypothesize that high performance of a company is associated with
high levels of customer satisfaction. At the same time it has to be true, however, that
customer satisfaction is not only monitored, but these findings are also actively used
by businesses to improve the quality of their products. It seems that the average or
below-average businesses monitor customer satisfaction formally or (with respect to
the way of operating and managing an enterprise) inappropriately, and they fail (or do
not want) to work further with the acquired information and to project it to the way of
running their business. The problem can also be a distrust of this information, or
unwillingness to changes (i.e. waiting for a response of competition).
In this context it is interesting and paradoxical that the vast majority of businesses
across clusters indicated that the acquired information concerning customer
satisfaction is reflected in the form of innovation in their products (the results were
not statistically significant, however). In the case of below-average enterprises it was
even 100% of the companies. It is therefore another argument supporting the claim
that below-average businesses do not evaluate their situation objectively.
The research shows that even the rate of product complaints in an enterprise is
essential for the relationship between quality and performance. It is interesting that in
the case of highly efficient businesses of cluster A the most frequent complaint rate is
between 2-3%, while it varies between 0-1% in the remaining firms (average and
below-average companies). Unless we want to accept the hypothesis of decreasing
objectivity of assessing the number of complaints in relation to performance, one can
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again think about the way of identifying complaints and further work with them. The
low rate of complaints can be related to the unwillingness to accept a complaint or
settle it in a positive way; however, this ultimately leads to frustration and customer
dissatisfaction and often also to losing them. This relationship can be (and will be)
examined in the second phase of the research into customer satisfaction of the
surveyed businesses.
The issue of complaints was followed by the issue focusing on product defects
(whether they are monitored, where they are found, and who determines them);
although the results were not statistically significant, they are important for the
clarification of the complaint issues. As in the case of complaints, product defects are
monitored across businesses (even a little more than complaints). Somewhat
surprising is the high rate of customer complaints revealed (approximately 50%)
while the most common defects were found in production (in approximately 80% of
cases). This finding therefore does not correspond with the claims of a low number of
complaints in below-average and average companies; on the contrary, it enhances the
hypothesis of lower objectivity of these respondents. The hypothesis of lower
customer satisfaction in clusters B and C supports by contrast the finding that defects
were more frequently found by customers themselves in these companies (42.3% of
enterprises in cluster C and 40.2% of enterprises in cluster B compared with 29.6% of
enterprises in cluster A). This finding is not be changed even by the fact that in other
cases the defects are most commonly revealed by specialized workers (73.2% of
enterprises in cluster B, 59.3% of enterprises in cluster A and 53.8% of enterprises in
cluster C).
It seems that systematic quality management is not crucial for high business
performance. On the other hand, considering the large number of businesses that
control quality systematically across the clusters, it is clear that systematic quality
control is important. So the question is what the term systematic quality control
includes, i.e. what is the way (quality) of this control in individual enterprises. It can
be hypothesized that it is substandard in below-average companies and outstanding in
above-average ones.
With regard to a follow-up question, which examined what made companies control
quality systematically, a significantly higher percentage of companies in clusters B
and C (compared to cluster A) indicated certification and legislation. It can be inferred
that these companies understand quality control primarily as certification, which
constitutes only a basis, or the lowest possible level of quality (setting the processes
and management systems). However, it is fair to mention that these results were not
statistically significant and that even average and below-average businesses reported
(similarly to highly efficient companies) mainly the pursuit of quality and customer
requirements as an incentive to control quality; nevertheless, they reported these two
indicators less often than highly efficient companies (in the case of the pursuit of
quality the difference was about 10%).
The monitored performance indicators suggest a surprising finding that companies
prefer non-financial indicators of customer satisfaction and complaints, between
which only one financial indicator – sales – was placed. The companies keep on
monitoring other financial indicators; however, the majority of the most frequently
used financial indicators is absolute (except for productivity). Ratios preferred and
recommended by financial analysts are used minimally. It therefore raises a question
to what extent are companies well and properly informed about their performance,
and how are they able to compare this performance with their competitors. In this
sense, we can ask a question whether the businesses make a comparison with
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competitors (in terms of performance) at all, since it can be inferred from the results
that they do it only minimally. Absolute indicators are inappropriate for such a
comparison.
It is surprising that despite the claimed emphasis on customer satisfaction and
production quality (including the connection of production quality with this
satisfaction), only an absolute minimum of businesses associate these indicators with
performance. Surprisingly, below-average businesses in cluster C realize this
connection more often, but on the other hand, they monitor these indicators less
frequently than the businesses in the other two clusters. Therefore what is important
for production quality (of a company) in terms of performance indicators is the
absolute financial indicators (basic, i.e. costs, sales, profit), supplemented with
productivity and the only non-financial indicator – employee register (which is not
frequently used otherwise).
It thus seems that the efficient businesses in cluster A associate the level of customer
satisfaction with the level of performance, and they do it more often than less efficient
companies in clusters B and C; however, they do not associate this satisfaction with
quality very much. Nevertheless, this is contrary to the previous findings. It can be
hypothesized that businesses (across clusters) are still little aware of the connection
between customer satisfaction, quality and business performance; it is true to a lesser
degree as business performance declines, this awareness grows. On the other hand, it
seems that less efficient companies do not go any further beyond realizing this
connection, i.e. it can be hypothesized that less efficient companies are less able to
project customer satisfaction to the quality of their products, no matter what they
think and say about it (especially how high it is). Again, we return to the hypothesis of
lower objectivity of respondents coming from less successful companies.
It is obvious that the problems of excellent enterprises in cluster A are different from
those in the other two clusters. These businesses have a problem with the size, and it
can be expected (also thanks to the composition of the sample) that they considered
themselves to be (relatively) small, or smaller, respectively, and with a smaller range
of provided services. Therefore it seems that an effort to satisfy a customer is higher
here than in the other two clusters, or that cluster A businesses understand this effort
as a problem to solve.
On the contrary, below-average companies have a problem with the cost of operation,
which implies a lower degree of efficiency, and as a consequence also of a lower level
of quality of an enterprise (or at least of the way it is managed). However, these
enterprises also perceive size as a problem, and in this respect we can probably say
about them the same as about excellent businesses in cluster A.
Below-average companies in cluster C also have the biggest problem with the cost of
operation, i.e. they can be characterized in this sense similarly to companies in cluster
C. The second biggest problem for them is the funding possibility, which is obviously
related to their below-average performance. The problem with the company size is a
common problem to all the clusters. Based on these findings we can accept the
hypothesis that a higher level of quality of an enterprise (or at least of the way it is
managed) leads to a higher level of business performance.
CONCLUSION
In terms of business performance, authors work in fact only with profitability
indicators (namely ROA) in connection with quality. This indicator (along with ROE)
was also crucial for the division of businesses into performance-based clusters. The
research results, however, show that significant deviations can be found even in the
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activity indicator (asset turnover). It is not very surprising as activity indicators (and
in particular it is asset turnover) are very closely related to ROA and ROE indicators.
It can be argued that the quantity of the asset turnover indicator proportionally affects
the quantity of ROA and ROE indicators. Significant (but smaller) differences can be
found in the liquidity indicator where the difference between excellent businesses in
cluster A and enterprises from the other two clusters is particularly apparent. In the
case of the indebtedness indicator, the results do not vary significantly at first sight;
however, it is impossible to overlook the negative impact of financial leverage in
cluster C below-average companies whose financial results and ROE are driven even
further into the red numbers by the (otherwise optimal) indebtedness. On the contrary,
excellent and average enterprises increase their financial results and ROE within their
optimal indebtedness.
In terms of assessing product quality, it is clear that businesses consider it very high.
The research suggests that the relationship of product quality to performance is
inversely proportional, i.e. higher product quality leads to a lower level of
performance as average and below-average businesses assess the quality of their
products higher than efficient businesses. It should be noted, however, that this
evaluation is subjective and it was conducted by the companies themselves. It is
therefore possible that less efficient businesses reported about the quality of their
products less objectively. This hypothesis will be subject to yet another part of the
research when these results will be confronted with customers’ opinions. Thus an
objective assessment of production quality will be possible through additional
research among customers of the surveyed companies.
The fact that the objectivity of the respondents could be a serious problem of the
research was reflected even in the evaluation of complaints and systematic approach
to quality. The systematic approach to quality also raises a question how the
respondents understand it. It seems that what many respondents (especially from the
less efficient businesses) perceive as this systematic management is gaining a
certificate and establishing a quality control department. However, this is obviously
not enough, i.e. the follow-up research will have to determine whether businesses use
any of the quality management systems such as EFQM Excellence, TQM, etc.
On the contrary, the level of company quality and quality control methods revealed
the cause of monitoring customer satisfaction when more efficient businesses
concentrated more often on feedback and improving product quality (as one without
the other is difficult to implement). On the other hand, the less efficient companies
were pushed to monitor customer satisfaction more frequently by the competition.
Weaknesses of businesses also revealed pressing problems and suggested which
companies can focus on product quality more than others. While the below-average
businesses solve problems with the costs of operation as well as how and from whom
they could obtain financial resources, more efficient companies can address the range
of services provided, how to satisfy customers better, and thus the quality of their
products. Therefore it seems that the basis of an efficient business is quality business,
i.e. quality management and management system, which will introduce rules in a
company and set the efficiency of resources, which the company uses, at a high level.
It then forms the basis for product quality and customer satisfaction, which will
project to high business performance.
As for the factors affecting quality, the research shows that they include the following
factors: the way of understanding quality, including its objective evaluation. The
second factor is the monitoring of customer satisfaction, including the causes that led
to this monitoring; this factor is obviously associated with the third factor, which is a
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quality control system, or its level and sophistication, including its actual use in an
enterprise, respectively. The fourth factor includes complaints and their monitoring,
which must be as objective as possible, and the fifth factor is the method of
monitoring performance, including the interconnection of the respective indicators
with product quality. Within the fifth factor, a very important indicator seems to be
customer satisfaction, the number of complaints, sales, costs and profits (or better
indicators of profitability).
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