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Modelling Customer Satisfaction of Smartphones
in the Czech Republic
DOI: 10.12776/QIP.V27I1.1818
Petr Suchánek, Radovan Sobota
Received: 2023-01-10 Accepted: 2023-03-05 Published: 2023-03-31
ABSTRACT
Purpose: The goal of this paper is to model customer satisfaction of smartphone
select manufacturers in the Czech Republic (CR). Furthermore, the paper aims to
model the factors which affect customer satisfaction of the smartphone.
Methodology/Approach: A questionnaire was sent to 1,063 respondents in CR
to collect data. Using structural equation modelling, relationships between factors
of customer satisfaction within three models of customer satisfaction of select
smartphone manufacturers were modelled.
Findings: Effects of all investigated factors of customer satisfaction were
verified, as well as all items which constituted the factors. Additionally, the
functioning of the factor of total satisfaction with dimensions of general
satisfaction and price tolerance was verified.
Research Limitation/Implication: The research is limited by its focus
exclusively CR, the number of manufacturers included in the research is rather
low and small number of factors and items included in those factors.
Originality/Value of paper: The models differed from each other in terms of the
strength and direction of the relationships between the factors, which has
implications for these recommendations. In general, each manufacturer has its
own strengths and weaknesses (factors) that affect customer satisfaction with its
product. Individual manufacturers can increase customer satisfaction by
strengthening the positive factors or by learning from their competitors and
eliminating or improving the factors that currently affect customer satisfaction
negatively.
Category: Research paper
Keywords: customer satisfaction; smartphones; hardware and software; design;
image
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1 INTRODUCTION
Customer satisfaction in the smartphone industry has been subject to intense
research in the last 20 years (see e.g. Turkyilmaz and Ózkan, 2007; Shin, 2014;
Haba and Hassan and Dastane, 2017; Shrestha, 2020). Nevertheless, certain areas
remain unexplored by contemporary research. One of these areas is the factors
affecting customer satisfaction, including mutual relationships between these
factors as well as between the factors and customer satisfaction as a whole. These
relationships have been researched from using customer satisfaction index in the
study by Turkyilmaz and Ózkan (2007) or Shin (2014), with the industry being
studied as a whole. In research by Kim et al. (2016), Putra et al. (2020) and Nath
and Saha and Hossain (2015), simple relationships between various factors and
customer satisfaction were examined. More complex relationships including
mutual relationships between investigated factors received attention in studies by
Shin (2015), Yazdanparast and Tran (2021), and Diputra and Yasa (2021).
The purpose of the paper is to find out which factors affect customer satisfaction
of the smartphone, i.e., which factors customers perceive as important and how
these factors affect satisfaction. Knowledge about these factors will allow
smartphone manufacturers to improve the focus of their innovations and improve
those parameters which are the most important from a customer satisfaction
perspective. This is beneficial, as high customer satisfaction affects customer
loyalty (Kim et al., 2016), i.e., the ability to retain a customer in the long-term.
Furthermore, customer satisfaction increases business performance (Morgan and
Rego, 2006) and thereby enables returns on investments. Proving an effect of
price tolerance on customer satisfaction as a component of satisfaction is
important for addressing questions regarding product pricing following
innovations in areas which affect customer satisfaction.
A model of customer satisfaction focused specifically on factors of satisfaction
relevant for smartphones has the potential to address the research gap outlined
above. In this paper, such a model is constructed. It included factors never used
together in a standalone model before. This represents an opportunity to
construct relationships between factors which have not been investigated before
and construct a unique model in this regard. Another potential direction of
research includes constructing customer satisfaction as a composite of price
tolerance and general customer satisfaction, which is also done in this paper.
2 THEORETICAL FRAMEWORK
There are several definitions of customer satisfaction. Perhaps the most
comprehensive definition is provided by Giese and Cote (2000, p. 15):
"Consumer satisfaction is a summary affective response of varying intensity,
with a time-specific point of determination and limited duration, directed toward
focal aspects of product acquisition and/or consumption". Customer satisfaction
in the smartphone industry specifically was studied by Turel and Serenko (2006)
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and Turkyilmaz and Ozkan (2007). In this paper, customer satisfaction is
constructed as a single factor, in order to more clearly isolate it from other factors
which affect it. This is in line with Shrestha (2020) and Panigrahi and Azizan and
Shamsi (2021).
An effect of price satisfaction on general customer satisfaction was found in the
smartphone industry (Bolton et al., 1999). Price satisfaction is not usually
included in research of customer satisfaction in the smartphone context.
Furthermore, a strong relationship was found between price satisfaction and price
tolerance (Pandey et al., 2020; Yol and Serenko and Turel, 2006), which is
investigated in the context of customer satisfaction in the smartphone industry.
A unique two-dimensional construct of customer satisfaction was created in this
paper, which contains not only the dimension of general satisfaction but also the
dimension of price tolerance. This factor of customer satisfaction corresponds in
its construction and content most closely to the single-factor multidimensional
construct by Galbreatha and Shum (2012) or Shrestha (2020) and Valvi and West
(2013). This factor was constructed as general customer satisfaction (the first
dimension) in line with the approach of Turkyilmaz and Ozkan (2007),
supplemented with the dimension of price tolerance in line with the approach of
Yol and Serenko and Turel (2006).
Customer satisfaction seems to be affected by a number of factors depending on
who investigated the concept and how. An appropriate approach lies in
constructing factors based on specific customer demands, i.e., parameters which
the customer considers important. Cerit and Kuctikyazici and Kalem (2014)
distinguish seven factors, another categorization is offered by Malaquias and
Silva Junior (2020), Hsiao (2013), Ahmad (2017) or Choudhury and Gulati
(2020). The studies above lead to the conclusion that a large number of items
were examined and subsequently (in some studies) categorized as parts of
factors.
Even the number and conceptualization of the factors differ from author to
author. A closer inspection reveals that two primary factors can be distinguish in
regard to technical parameters: hardware and software (Cerit and Kuctikyazici
and Kalem, 2014; Malaquias and Silva Junior, 2020). According to Kim et al.
(2015) and Swanson and Taylor (2011), the primary hardware factors affecting
customer satisfaction are network card (connection speed) and processor
performance. One of the most often evaluated features and significant
satisfaction parameters is battery life (Cerit and Kuctikyazici and Kalem, 2014;
Kim, 2011). This parameter is related to charging speed, which also significantly
affects customer satisfaction (Ahmad, 2017; Kanagaraju and Karthikeyan, 2021).
In relation to the growing trend of using smartphones to consume audio and
video content, the importance of sound quality (Kochkin, 2002) and camera
quality (Song, 2018; Ahmad, 2017) has increased.
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Based on the above mentioned, the following hypothesis was stated:
HI: Satisfaction with a smartphone's hardware has a positive effect on customer
satisfaction.
Software may then include the operating system, security and interconnectivity
with other devices. A significant factor that directly (Kim et al., 2015) or
indirectly (Shin, 2015) influences customer satisfaction is the operating system.
Operating system is one of the factors which affect smartphone choice and
purchase (Rajasekaran and Cindhana and Anandha Priya, 2018). The second
factor is device security. Based on research by Roy and Halevi and Memon
(2015), the majority of users protect access to their devices with some form of
authentication and are concerned about their data. The third factor addresses
satisfaction with the ecosystem of devices connected to the smartphone. Research
by Kim et al. (2015) and Chang et al. (2011) shows that ecosystems of various
manufacturers differ from each other. Based on the above mentioned, the
following hypothesis was stated:
H2: Satisfaction with a smartphone's software has a positive effect on customer
satisfaction.
Considering the large number of hardware items, design items were separated
into their own factor. This is contrary to research by Malaquias and Silva Junior
(2020), but in accordance with the approach of Sabir (2020) and Hsiao (2013).
Two factors addressed smartphone design. The first focused on the look of the
device (appearance), i.e., satisfaction with size, weight, materials and style. The
look of the device is therefore an important factor of customer satisfaction (Sabir,
2020; Haverila, 2011; Barkhuus and Polichar, 2011). The second factor was
connected to device durability, such as resistance to scratching or breaking,
water-resistance etc. Durability was suggested as a criterium for device choice by
I§iklar and Buyukozkan (2007). Ahmad (2017) shows that the effect of durability
on satisfaction is quite strong, Choudhury and Gulati (2020) on the other hand
found a medium effect. Based on the above mentioned, the following hypothesis
was stated:
H3: Satisfaction with a smartphone's design has a positive effect on customer
satisfaction.
Research of customer satisfaction in the smartphone industry often includes
brand image (Turkyilmaz and Ozkan, 2007; Kusumah, 2018; Abubakar and
Sugito, 2019; Diputra and Yasa, 2021). Although image and its parameters are
not among the technical parameters such as hardware and software, it is still a
significant factor, affecting customer satisfaction even more than price
(Abubakar and Sugito, 2019) and quality (Kusumah, 2018). Kim et al. (2016)
examined the effect of corporate image on customer satisfaction in parallel with
product features and found corporate image showed the second most significant
correlation with customer satisfaction, after technical soundness of the product.
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Further two factors were concerned with manufacturer image and its effect on
customer satisfaction. The first concerns brand image, which may be defined as
how a current or future customer perceives the brand and what they connect with
the brand (Haba and Hassan and Dastane, 2017). Research shows that brand
image positively affects customer satisfaction (Chusnaini and Rasyid and
Candraningrat, 2022). The second factor focused on the emotional connection
between the customer and the brand (corporate image). During brand image
building, it is important to capture the customer with an emotional experience
(Nanda et al., 2008). Emotional connection with the brand in the smartphone
industry and its effect on customer satisfaction was confirmed by Kim et al.
(2016). Based on the above mentioned, the following hypothesis was stated:
H4: Satisfaction with a smartphone's image has a positive effect on customer
satisfaction.
Research shows that consumers rate mobile services from a utilitarian as well as
hedonic perspective (Novak and Hoffman and Duhachek, 2013; Van der Heijden,
2004). According to Zhang et al. (2014), the type of service affects the general
purpose of features and services which will be used (utilitarian perspective).
Furthermore, they examined the extent to which emotions affect choice and
rating of a device by its user (hedonic perspective). Both perspectives were
shown to affect customer satisfaction (Shin, 2015). Based on the above
mentioned, the following hypothesis was stated:
H5: Customer's perception of utilitarian or hedonic value of the device has a
positive effect on customer satisfaction.
3 METHODOLOGY
3.1 Questionnaire and Statistical Methods
An online questionnaire was used to collect data for the quantitative research.
This questionnaire contained closed questions focused on demographic data (age,
gender, highest achieved education, job and size of town). These were
supplemented with the type of smartphone the respondent owned. These
questions were used to judge the representativeness of the sample of respondents.
Another set of questions addressed the investigated variables and factors hardware,
software, design, image, utilitarity/hedonicity and total satisfaction.
These questions were evaluated on five-point Likert scales, in line with research
by e.g. Shrestha (2020) and Diputra and Yasa (2021).
The structural equation modelling method (SEM) was used to analyse the data.
Considering the large number of latent variables used to saturate the individual
variables, as well as the number of factors and modelled mutual relationships, the
sample size is appropriate for using the SEM method (see Wolf et al., 2013;
Nachtigalla et al., 2003).
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A number of tests were used to verify the robustness of the models. First, the
Comparative Fit Index (CFI) was used. According to Bentler and Bonett (1980),
values should not fall under 0.9. The second test used was the Tucker-Lewis
Index (TLI). The minimal recommended value of TLI is 0.9 (Nazim and Ahmad,
2013; Bentler and Bonett, 1980). Next, Standardized Root Mean Square Residual
(SRMR) was used. Generally, the maximum acceptable SRMR value is 0.08 (Shi
and Maydeu-Olivares and DiStefano, 2018). Finally, the Root Mean Square Error
of Approximation (RMSEA) was used. The maximum acceptable value for
RMSEA is 0.08 (Hu and Bentler, 1999).
3.2 Research Sample
A research sample consisted of a total of 1,063 respondents from throughout the
Czech Republic. The sample of respondents consisted exclusively of end
consumers who purchased a smartphone for personal use. Data collection took
place in November and December 2021. The respondents were contacted using
various channels (Facebook, database of email addresses).
Comparison of chosen demographic characteristics of the sample and of the
Czech Republic in 2020 (most recent available data) can be viewed in Tab. 1.
There is a difference in gender composition between the sample and the Czech
population, it is small enough (3 percentage points) to consider the sample
representative. The situation is similar for samples by manufacturer: Apple -
51% men and 49% women, Samsung - 48% men and 52% women, Xiaomi -
52% men and 18% women.
Table 1 - Demographic Characteristics (Preparing by Authors using Czech
Statistical Office (2019, 2020a, 2020b))
Demographic characteristics Number of respondents in the sample CRDemographic characteristics
Absolute % %
Gender Male 554 52% 49%Gender
Female 509 48% 51%
Size of town of
residence
Under 1,000 residents 166 15.5% 17%Size of town of
residence
1,000-6,000 residents 232 22 % 12%
Size of town of
residence
6,000-10,000 residents 92 8.5% 9%
Size of town of
residence
10,000-25,000 residents 97 9% 9%
Size of town of
residence
25,000-50,000 residents 111 10.5% 12%
Size of town of
residence
Over 50,000 residents 365 34.5% 41%
The sample can also be considered representative regarding the size of the
respondent's town of residence, where the sample structure matches the structure
of Czech population in four parameters out of six (the maximum difference is
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1.5 percentage points here) and only two parameters differ in a more significant
way (6.5 and 10 percentage points respectively). The situation is similar again for
the samples by manufacturer: Apple has the largest deviation for the four
parameters mentioned 1.7%, for the other two 8.1% and 9.4%; Samsung - 1.3%,
5.7% and 11.2%, Xiaomi - 1.3%, 6.1% and 10.8%.
Regarding age structure, education and job, the differences between the sample
(including samples by manufacturer) and Czech population are large enough to
reject representativeness of the sample in these parameters. These other
demographic characteristics are shown in Tab. 2.
Table 2 - Additional Demographic Characteristics
Demographic characteristics Number of respondents in the sample CRDemographic characteristics
Absolute % %
Age group Under 14 20 2% 16%Age group
15-24 257 24% 9%
Age group
25-44 531 50% 28%
Age group
45-64 227 21.5% 27%
Age group
65 and older 28 2.5% 20%
Education Primary 128 12% 13%Education
Secondary without state exam 155 14.5% 33%
Education
Secondary with state exam 573 54% 34%
Education
Tertiary 207 19.5% 20%
Job Student 237 22% 16%Job
Unemployed 26 2.5% 1%
Job
Employee 637 60% 49%
Job
Entrepreneur 130 12% 12%
Job
Pensioner 33 3% 22%
Representativeness of the sample was further checked from the perspective of
smartphone ownership in the Czech market (see Tab. 3). Although the Czech
smartphone market contains hundreds of different smartphone models, it is
divided between four largest manufacturers, who collectively control almost 90%
of the entire market.
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Table 3 - Brands Most Represented in the Sample (Preparing by Authors Using
Statcounter (2021))
Manufacturer Number of respondents CRManufacturer
Absolute % %
Apple 298 28% 27.91%
Samsung 272 25.6% 22.9%
Xiaomi 255 24% 23,38%
Huawei 109 10.3% 13.63%
Other 129 12.1% 12.18%
Characteristics in Tab. 3 shows that the sample is representative regarding the
structure of smartphones sold, as the relative differences between smartphone
brands represented in the sample and in the population is minimal, with the
greatest difference reaching only 3.33 percentage points (in case of Huawei).
4 RESULTS AND DISCUSSION
In this section, results of the three partial models of customer satisfaction for the
brands Apple, Xiaomi, and Samsung will presented, followed by the summary
model of satisfaction for the entire industry.
4.1 Apple
The model of customer satisfaction for Apple smartphones is visualized in Fig. 1.
CFI and TLI values were both 0.999, RMSEA value was 0.043 and SRMR was
0.057. P-value of the model: p < 0.01. Each of the relationships within the model
is statistically significant, but some of the coefficients (hardware and
utility/hedonic) are very low, their impact on Apple customer satisfaction is
therefore minimal (negligible) and so we will not deal with them further.
Every formulated hypothesis can be considered confirmed, except H3, which is
concerned with design. It is interesting that the relationship between design and
customer satisfaction is negative, i.e., that design reduces customer satisfaction.
Although this relationship is weak, it is statistically significant. No conclusions
can be made whether design (including looks and durability) is less important for
the Czech customer. On the other hand, the research shows that it is the third
most important factor (factor with the third strongest influence on customer
satisfaction).
The influence of the other factors on customer satisfaction is positive, with
software having the strongest effect, followed by image. The factor with the
weakest effect are hardware and the utilitarity vs. hednocity.
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Security (1.13) and ecosystem (1.06) have a stronger effect on total satisfaction
than the operating system itself. This may be caused by the fact that iOS has been
in gradual development for years. On the other hand, security and ecosystem
have seen more radical changes in Apple smartphones. Mohamed and Patel
(2015) argue that iOS is more resistant to attacks than Android, especially due to
restrictions within the only application marketplace - the App Store. Satisfaction
with security has been growing over recent years primarily due to 3D face
scanning technology Face ID, which, according to Bud (2018).
As for the Apple ecosystem, Bosch (2009) argued that the most important factor
in the success of software ecosystems is the number of customers using the
operating system who are available to developers for monetization. Furthermore,
according to Tilson and Sorensen and Lyytinen (2012), contrary to iOS devices,
Android devices have access to multiple stores. Some websites offer unverified
and unofficial applications outside of the Android Google Play store. Owners of
iOS devices therefore encounter a lack of opportunities to install apps.
Figure 1 - Partial Model of Customer Satisfactionfor Apple Smartphones
The emotional relationship of an individual with the Apple brand has a stronger
effect than the brand itself, which is not surprising considering the longevity of
the device and its support. As asserted by Eaton et al. (2015), the iOS system
receives constant incremental changes, which made it an example for the industry
and many competitors from among Android manufacturers attempt to replicate
Apple's success by prolonging the software support for their devices.
Cusumano (2010) argues that from a long-term perspective, the most valuable
asset of a company is and will remain its online services and platforms. The
negative relationship between Apple smartphone design and customer
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satisfaction can be understood from several perspectives. Apple's results
correspond to some extent with the fact that Apple aspires to a specific design
(Dospinescu, Florea, 2016), which is not very appreciated by Czech customers.
On the other hand, the lower weight of the influence of this indicator on
satisfaction found in this research corresponds with Apple's focus on other
factors, especially image (Dospinescu and Florea, 2016).
Smartphones in general increase in size, weight, and volume year by year.
According to Dospinescu and Florea (2016), the shape, size and material of the
smartphone are among the most significant factors in satisfaction with the design.
A study by Evelyn (2019) demonstrates dissatisfaction of customers with Apple
design especially due to construction faults. Customers (including Czech) also
perceive the Apple smartphone as a luxury brand with a higher price (Kapferer,
2016; Malá, 2020), they perceive negatively that at first glance this smartphone
does not differ (in appearance or endurance) from others.
4.2 Xiaomi
Customer satisfaction model for Xiaomi smartphones is visualized in Fig. 2. CFI
values reach 0.993, TLI values reach 0.99, RMSEA reaches 0.077 and SRMR
reaches 0.075. P-value of the model: p < 0.01. Each relationship in the model is
statistically significant, but some of the coefficients (image and utility/hedonic)
are very low and their impact on Xiaomi customer satisfaction is therefore
minimal (negligible).
Figure 2 - Partial Model of Customer Satisfactionfor Xiaomi Smartphones
Every formulated hypothesis can be considered confirmed, except H2 and H5,
which were concerned with software and utilitarity/hedonicity. It is interesting
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that the effects of both software and especially utilitarity/hedonicity on customer
satisfaction are negative. It runs contrary to previous research, which asserted
that the effect of utilitarity/hedonicity on customer satisfaction is positive,
regardless of preference for either hedonicity or utilitarity (Shin, 2015). The
negative effect of utilitarity/hedonicity is rather very weak and so we will not
deal with them further (together with image). In contrast, the negative effect of
software is the second strongest.
It seems that software does not bring greater satisfaction to customers (especially
security followed by ecosystem and operating software), which then projects into
lower customer satisfaction. This brings about the question whether the lower
satisfaction is caused by inadequate price or by specific parts of the software.
The effect of the other factors is positive, with design having the strongest effect
(especially looks, followed by durability). This is followed by hardware
(especially performance, connectivity, audio and photo). General satisfaction of
the respondent with their device also has a relatively strong effect, though
significantly weaker than in the case of Apple. Effect of price tolerance is
comparable to effect of general satisfaction (equivalent to Apple's case).
Based on results of the model for Xiaomi, the conclusion can be made that
owners of these smartphones perceive their satisfaction completely differently
than Apple users. Performance is the most important variable in the hardware
factor (1.05), with the ecosystem variable being the least important in the
software factor. This is primarily because Xiaomi is a young company in CR
(from 2016) - its ecosystem exists, but it is not based on software and
interconnected devices through its own operating system. In this aspect, Xiaomi
is dependent on Google (Martinásek, 2021). According to Tong and Guo and
Chen (2021), Xiaomi creates an ecosystem through contracts with gadget
manufacturers which allows it to quickly penetrate markets, as opposed to Apple,
which focuses on a closed ecosystem of devices and operating systems. Xiaomi's
ecosystem can work with devices from Apple and other manufacturers. While in
China Xiaomi (in terms of software and especially the ecosystem) has a similarly
strong position as Apple in the CR (and in the world) (Sun and Fah, 2020), in the
CR its position is significantly weaker and it cannot take advantage of the
benefits (especially the ecosystem = connecting other devices) as in China
(Martinásek, 2021). This is the main reason for the strong negative impact of
software on Xiaomi's customer satisfaction in CR.
There is a significant difference in emotional bonds with brand between Xiaomi
and Apple. The emotional bond to Xiaomi is more than eight times worse than to
Apple. Though Xiaomi does maintain a community, it has yet to build up a
customer base as loyal as Apple. Additionally, Xiaomi includes a protective
silicone case in the packaging of its smartphones, which may explain the positive
effect of design on customer satisfaction. Garg et al. (2018) confirm that owners
of Xiaomi smartphones are very satisfied with the quality, looks and design.
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Badiangsie and Lapian and Tumbuan (2019) confirms strong customer
satisfaction with the design (especially the appearance) of Xiaomi.
Software has a strong negative effect on Xiaomi customer satisfaction, which
may be caused by the Android based MIUI custom R O M created by a
community in China. This lower satisfaction is caused by faults in this expansion
over recent years, where even stable versions of the operating system
encountering trivial glitches. According to Pee et al. (2019), MIUI glitches are
fixed in Chinese beta versions of the operating system, where each user may
receive a ranking for reporting bugs, which allows them to vote on prioritization
of other bugs to be fixed.
4.3 Samsung
Model of customer satisfaction with smartphones for Samsung is visualized in
Fig. 3. CFI value reached 0.995, TLI value reached 0.993, RMSEA is 0.073 and
SRMR is 0.055. P-value of the model: p < 0.01. Each relationship in the model is
statistically significant, but some of the coefficients (design and utility/hedonic)
are very low and their impact on Samsung customer satisfaction is therefore
minimal (negligible).
Figure 3 - Partial Model of Customer Satisfaction with Smartphones
for Samsung
Each formulated hypothesis was confirmed except H3 and H5, which have to do
with design and utilitarity/hedonicity, however, their impact on customer
satisfaction is negligible and so we will not deal with them further.
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Effect of the remaining factors is positive, with hardware having the strongest
effect (especially audio, connectivity, performance and camera). This is similar
to Apple's case. Next in line is image (brand image followed by emotions). The
effect of software can be classified as weaker (security is the most important item
in this factor). General satisfaction with the device also has a relatively strong
effect on total satisfaction (this is stronger than in Xiaomi's case, but
significantly weaker than in Apple's case). Effect of price tolerance is weaker
(same as in Apple's and Xiaomi's cases).
Owners of Samsung devices value hardware quality much more (six times as
much) than Apple users. One of the key items which affected satisfaction with
Samsung's hardware was performance (1.11). However, Halpern and Zhu and
Reddi (2016) warn that current techniques of smartphone-specific processor
development have poor future prospects, as they do not fit within temperature
limits and energy-consumption limits. Samsung as a company should consider
investment into new processor development, or focus on software optimizations.
Contrary to Xiaomi, Samsung has fewer issues with its Android overlay One UI
than Xiaomi, which is reflected in the positive effect of software on customer
satisfaction. According to Thomas and Devi (2021), MIUI in recent years had
problems with a lagging basic launcher, with short battery life, overheating,
security updates and with GPS. One UI has features similar to MIUI, however,
using One UI requires owning more expensive hardware within the price
category compared to Xiaomi.
Samsung's brand image is positive, mainly due to the brand image item, with a
comparatively lower effect of emotions. Similar results (including the strength of
the effect of image on customer satisfaction) were obtained by Abubakar and
Sugito, (2019). The strength of the effect of image on customer satisfaction in
CR is higher for Samsung than for Apple (although the effect is strong and
positive in both cases), which corresponds with the results of Havard et al.
(2021).
4.4 The Smartphone Industry
The summary model of customer satisfaction for the entire industry is visualized
in Fig. 4. CFI value reaches 0.996, TLI is 0.995, RMSEA is 0.06 and SRMR is
0.046. P-value of the model: p < 0.01. Each relationship within the model is
statistically significant, but some of the coefficients (utility/hedonic) are very low
and their impact on smartphone industry customer satisfaction is therefore
minimal (negligible).
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Figure 4 - Summary Model of Customer Satisfaction in the Smartphone Industry
Each hypothesis was confirmed except H3 and H5, concerned with design and
utilitarity/hedonicity.
In Tab. 4, you can see a comparison of the strength of the impact of each of the
factors examined on customer satisfaction by manufacturer and across the
industry.
Table 4 - The Strength of the Impact of Each Factor on Customer Satisfaction by
Manufacturer and Across the Industry in the Czech Republic
Manufacturer Hardware Software Design Image Utility/hedonic
Apple 0.07 0.42 -0.17 0.26 0.07
Samsung 0.45 0.12 -0.01 0.34 -0.09
Xiaomi 0.46 -1.02 1.09 0.04 -0.08
Smart phone industry 0.24 0.50 -0.25 0.24 -0.01
The negative effect of design on satisfaction is in line with the cases of Apple
and Samsung. The relationship between utilitarity/hedonicity is similar to the
cases of Xiaomi and Samsung. The negative effect of design is relatively strong,
the effect of utilitarity/hedonicity is very weak and therefore there is no purpose
in dealing with it further. Both negative effects are statistically significant.
Respondents' satisfaction with design (primarily looks followed by durability) is
low, which reduces to total satisfaction with smartphones. According to Cordelia
et al. (2021), the entire industry should consider using stronger materials for the
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QUALITY INNOVATION PROSPERITY / KVALITA INOVÁCIA PROSPERITA 27/1 - 2023 133
rear side of the smartphone, as well as designing their devices with greater
impact resistance in mind. Thus, it seems that the low satisfaction with design is
not caused by price, but directly by the design itself (especially durability). This
is because price tolerance has a positive effect on total satisfaction (Yol and
Serenko and Turel, 2006), from which it follows that it is relatively high.
The effect of other factors on satisfaction is positive, with software having the
strongest effect (especially security), in line with Apple's case; followed by
image (primarily brand image). The effect of hardware is the same as image
(within this factor, performance, audio and connectivity are the most important
items).
Research has confirmed the positive relationship of image on customer
satisfaction (Chusnaini and Rasyid and Candraningrat, 2022; Kim et al., 2016).
This relationship is quite strong, both in the industry model and in Apple and
Samsung smartphone models, which corresponds to the strength of the image
itself (especially brand image) of the three strongest manufacturers: Apple,
Samsung and Huawei, and explains the weak impact of Xiaomi a customer
satisfaction due to weak brand image (Valjaskova and Krai, 2019).
5 CONCLUSION
The constructed models of customer satisfaction in the smartphone industry
proved the effect of hardware, software, design, image, and utilitarity/hedonicity
on customer satisfaction. The impact of design and utilitarity/hedonicity was
generally negative (Samsung Xiaomi), but very small to negligible (which was
also reflected in the smartphone industry model) and therefore not relevant to
take into account.
The negative effect of design (Apple, Samsung) is primarily connected with
materials and size, as well as with the smartphone's durability. It seems that users
are not satisfied with these attributes, which projects into a negative effect on
total satisfaction. The effect of this factor is relatively weak (except Xiaomi).
This means that durability and construction are very important parameters for
(increasing) customer satisfaction only form Xiaomi. The effect of design is
relevant for Apple, where this negative effect is the third strongest, but less for
Samsung, where it is quite weak. In Xiaomi's case, design has the strongest and
most positive effect, meaning that other manufacturers can take inspiration from
this company's design. However, research results with smartphone satisfaction
show that the influence of durability is moderate and appearance is weak but
positive (Malaquias et al., 2020).
The influence of software is positive for Apple (it is the strongest factor in this
model), the influence is weaker for Samsung (it is the third strongest factor in the
model) and the strongest factor is in the Xiaomi model, where its influence is
negative. The research shows that software is very important for smartphone
users' satisfaction, and it needs to function perfectly in order to increase
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134 QUALITY INNOVATION PROSPERITY / KVALITA INOVÁCIA PROSPERITA 27/1 - 2023
satisfaction. For Xiaomi, this means improving its software to eliminate defects
or at least minimize their number and severity so that customers start to perceive
it positively.
There are also significant differences in the hardware and image factors. In the
case of Samsung, hardware is the dominant factor and image is the second
strongest. For Xiaomi, the strong influence of hardware is confirmed, but the
influence of image is the smallest. In the case of Apple, the opposite is true hardware
has the weakest influence and image the second strongest. The research
shows that both of these factors are important for Samsung's customer
satisfaction, for Xiaomi, hardware is more important and for Apple, image is
more important. This confirms the strength of brand image, where the top three
brands are Apple, Samsung and Huawei, which in the case of the Czech Republic
has also influenced the whole smartphone industry.
The results show the unique situation of the examined manufacturers in terms of
the direction and strength of the relationships between factors and satisfaction.
Apple's customer satisfaction is most influenced by (positive) software and
image and (negative) design, while Xiaomi's customer satisfaction is most
influenced by (positive) design and hardware and (negative) software. Samsung's
customer satisfaction is most influenced by (positive) hardware, image and
software.
Furthermore, the research reveals a two-dimensional approach to customer
satisfaction as viable, where general satisfaction was supplemented by price
tolerance. The strength of price tolerance (relatively significant compared to
general satisfaction, in partial models for Samsung and Xiaomi) proves the
significance of this variable not only in models of customer satisfaction, but also
in satisfaction as its own factor.
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0613.
ABOUT AUTHORS
Petr Suchánek0 0 0 0
"0 0 0 2
"2 6 0 6
^0 4 0
(P.S.) - Assoc. Prof., Masaryk University, Faculty
of Economics and Administration, Brno, Czech Republic, e-mail:
Petr.Suchanek@econ.muni.cz.
Radovan Sobota0000
"0001
"9813
"969X
(R.S.) - Ing., Masaryk University, Faculty of
Economics and Administration, Brno, Czech Republic, e-mail:
455506@mail.muni.cz.
AUTHOR CONTRIBUTIONS
Conceptualization, R.S. and P.S.; Methodology, R.S..; Software, R.S.;
Validation, R.S.; Formal analysis, R.S. and P.S.; Investigation, R.S.; Data
curation, R.S.; Original draft preparation, P.S.; Review and editing, P.S.;
Visualization, R.S.
CONFLICTS OF INTEREST
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of
the manuscript, or in the decision to publish the results.
0© © 2023 by the authors. Submitted for possible open access publication under the
terms and conditions of the Creative Commons Attribution (CC-BY) license
(http://creativecommons.Org/licenses/by/4.0/).
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