Understanding the consumer’s channel selection process: Cross-generation
differences in channel perception at pre-purchase stage
Marcin LIPOWSKI
email: [email protected]
Ilona BONDOS
email: [email protected]
Maria Curie-Sklodowska University, Faculty of Economics, Marketing Department
M. Curie-Sklodowskiej Square 5, 20-031 Lublin, Poland
Abstract. This study investigates the cross-generation differences in channel
perception at the information search stage. Two generations of Polish consumers,
Baby boomers (n=357) and generation Y (n=356), were included in the multi-channel
analysis restricted to three channels - offline, online and phone channel. With
information obtained from CAPI, we have found that perceived risk is a relevant
determinant of intention to use online channel as a source of information about
services only for younger generation, this group also perceives higher media richness
of all analyzed channels and demonstrates the higher intention to use the phone and
online channel. This research fills a gap in the literature since previous studies have
mainly focused on the one specific generation or consumers in general. Our
contribution is also an attempt to generalize the results to certain service categories
(financial services, telecommunications and transport), not just one particular service
and service supplier. Results indicate some interesting implications.
1. INTRODUCTION
Consumer information search behavior has a long tradition of being a useful
mechanism for understanding consumer shopping behavior, including consumer
choice, and choice processes (Maity, Dass, Malhotra, 2014). Undoubtedly
researchers have given substantial attention to the information search behaviour of
consumers because of its primacy in consumer decision making (Utkarsh and
Medhavi, 2015). In the literature on the multi-channel sales dominate the analysis
taking into account three channels - physical stores, websites and direct marketing
channels – catalogs (Verhoef, Kannan and Inman, 2015), increasingly appreciated is
the importance of the mobile channel in the form of applications (Xu et al., 2014). Our
research include three channels: offline, online and phone channel, what is more, the
study refers to the process of acquiring information about the service in terms of
intergenerational differences. This article is an attempt to identify the impact of the
customer age (especially the generation of baby boomers, and the Y generation) on
the importance of four factors for the perception of the channel as a source of
information about services. These factors are: perceived media richness, perceived
information quality, perceived cost and perceived risk. To the best of our knowledge,
no previous research has analyzed the perception of different channels as a source
of information about services in the context of customers' age understood in this
manner. According to us, the novel is an attempt to generalize the study and
conclusions for certain service categories (financial services, telecommunications
and transport), not just one particular service. The range of services was made based
on the results of the preliminary examination - diary observation of the way of using
service by consumers (Lipowski, 2015).The majority of studies focuses on comparing
channels on the stage of service purchasing. We have undertaken an attempt to
analyze the impact some factors to select a channel as a source of information about
services on the pre-purchase phase. It is common ground, there is crucial importance
of the pre-purchase stage for consumer behavior at the purchase stage, especially
under conditions of multi-channel sales.
The paper proceeds as follows: the next section discusses stage of searching
for information in multi-channel environment as well as media richness, perceived
quality and perceived risk and perceived cot as the main factors affecting channel
selection. The sections that follow, present research methodology, the hypotheses
and some findings. The paper ends with theoretical and managerial implications of
the findings, and proposals for future research.
2. Literature Review
2.1. Search for information in a multichannel environment
Researchers underline the popularity of pre-purchase information search as a one of
the most widely investigated topics in consumer research. Beyond doubt, consumers
search for information about the product prior to purchasing to reduce the perceived
risks associated with purchasing a product or service (Rijnsoever, Castaldi and Dijst,
2012). Because services are generally higher in experience and credence quality,
there is more risk in purchase (Elliott, Fu, and Speck, 2012). Such a finding is
consistent with preliminary findings suggest that there are important differences in the
depth and breadth of information searches for services compared to searches for
tangible goods (Huang, Lurie and Mitra, 2009).
Researchers had already acknowledged the distinguishing characteristics of
services that have an influence on consumer behaviour (Utkarsh and Medhavi,
2015). Lee and Yang (2013) note that due to unique features of service including
intangibility, heterogeneity, and inseparability of production and consumption, service
quality has been identified as an abstract and elusive construct. What is more, the
perception of service quality, being similar to customers’ attitudes, is used to provide
an entire evaluation for products chosen by consumers (Wu and Chan, 2011). If so,
the stage of searching for information about the service becomes even more
important for consumers. As is apparent from a meta-analysis conducted by Blut et
al. (2015), information quality of the websites is one of the vital construct of overall eservice quality. In the same vein say Wolfinbarger and Gilly (2003) who pointing to
the phase of service information search as a constituent element of the concept of eservice quality.
Stage of searching for information can also be analyzed in terms of
marketing channels – that approach was adopted in the article. How the process of
seeking information is seen in each of every available marketing channels. Certain
information channel has characteristics that make it more attractive to some
consumers compared to other channels. Wilkström (2005) notes that in the search for
some information may be more extensive, accurate, or visually stimulating on one
channel than it is on the other. Kim and Ratchford (2012) claim that, because of the
direct relevance of how consumers use information channels to a firm’s design of its
communication strategy, it is important to understand how consumers allocate their
time across different information sources. From the marketer's perspective, obvious
benefits in messaging, budgeting, and competitiveness arise from understanding the
search process and shaping it toward one’s own products (Singh, Ratchford and
Prasad, 2014).
It is also important to notice some danger connected with popularity of
Internet as a source of information – the more often consumers qualify the Internet as
a source of information rather than shopping venue, the greater may be the risk of
leaving the online channel in order to purchase in another one (Verhoef, Neslin and
Vroomen, 2007). It is associated with the attitude of research shopper – consumer
who uses several channels in one purchasing process. The most common channel
switching behavior occurs when consumers use the Internet to search and then buy
in an offline retail store (Chiu et al., 2011). However, the problem is when research
shopper is competitive not loyal – the consumer does not only change the channel,
but also change the seller (search: channel A of company 1and buy: channel B of
company 2) (Neslin and Shankar, 2009). This cross-channel free-riding practice can
be limited by the consumer belief that searching for product information from an
online store and purchasing from a brick-and-mortar store a hassle (Chiu et al.,
2011).
According to Birgelen, Jong and Ruyter (2006), a phone channels as well as
online channel can be a feasible alternative for onsite employees for services that
require more special attention. However, only customers perceive well-performing
phone facilities (i.e., call centers) and e-functionality reduces the necessity of having
to go to a local office. In the context of redirecting consumers to channels their
preferred service provider, this is a very important conclusion. A limitation of negative
effects resulting from forced (or voluntary) customers migration is possible while
ensuring adequate standards in other channels. Some empirical research that
investigated effects of channel elimination on purchase incidence has been
conducted and findings have been shown by Konus, Neslin and Verhoef (2014). But
Trampe, Konuş and Verhoef (2014) indicate that most recent firm efforts attempt to
steer customers to preferred channel (usually the online channel) not only for
purchases but also for the information search and after-sales phases of the shopping
process. This can have a great influence on searching information behavior because
of forcing customer to use a specific channel. There is a risk that even channel
assessed as a useful for information searching may be unacceptable by consumers
because of the compulsion to use it. What is more, even customers who already use
the firm-preferred e-channel experience reactance when they are forced to use the echannel or are punished for using the incumbent channel (Trampe, Konuş and
Verhoef, 2014). Also Ansari, Mela and Neslim (2008) emphasize that the notion that
migration is unqualifiedly positive because it lowers costs and increases demand
should be tempered by the admonition that it can be negatively associated with longterm purchase patterns.
According to Colby and Parasuraman (2003), due to retailers’ increasing use
of technological tools, the traditional modes of service delivery have been substituted
or enlarged by technology. The goal is to offer consumer better access to services
via various channels and to better meet consumer demand and increase consumer
satisfaction (Bitner, Ostrom and Meuter, 2002). This idea is strictly connected with
the core goal of multichanneling. The response to the proliferation of marketing
channels is the behavior of marketers who have developed multichannel
segmentation schemes to evaluate how consumers behave during the information
search and purchase stage (Neslin et al., 2006). It is important noticing the
phenomenon multichanneling at the stage of pre-purchase information search - as
Kumar and Venkatesan (2005) noticed, customers tend to look for information on
complex products online but prefer to purchase them after consulting a company
representative in person or by phone sales. Kim and Lee (2008) note that perceived
usefulness of a multi-channel retailer would increase when an Internet retail site
offers in-depth information about customer services attributes. Thus, in terms of
multichanneling there is no always one and only channel information, many of them
are used by consumers during one process of information gathering. Another study
indicates an interesting issue – the notion that customers’ relative preferences for
channels are contingent on type of activity, namely, its volume and complexity
(Sousa et al., 2015). Researchers recommend that absolute channel preference
effects should be changed to one of preference effects being contingent on type of
activity: some channels will be seen as superior for some activities, but not for other.
Of course it is possible to differentiate among consumers the extreme segments
(pure offline and pure online) and some multichannel shopper groups – (Elliott, Fu
and Speck, 2012) these are the dual-search offliners and cross-channel offliners. The
researchers show that even in a particular segment of consumers (baby boomers)
conflicting results regarding the sources of information used are available and the
cause of discrepancies may be the type of product (tangible goods versus services)
(Nasco, Hale and Thomas, 2012).
2.2.
Marketing channels and its customer perception
Taking into account consumer behavior at the first stage of the purchasing
process, one of the channel attributes evaluated by the consumer is perceived quality
of the information obtainable in a particular channel. The greatest research attention
in this area was devoted to online channel.
Internet has become an important resource for consumers to search for
products and prices (Bodur, Klein, Arora, 2015). Park, Chung and Yoo (2009) say
that Internet has often been acclaimed as a mighty tool with huge possibilities to
modify consumer information search behaviors. What is more, the Internet provides a
great deal of information which varies dramatically in terms of quantity as well as
quality. Ratchford, Talukdar and Lee (2001) explain why there are likely to be
considerable differences across consumers in the degree of use of the Internet as an
information source - because there are differences between consumers in respect to
their skills and access, and because learning to use the Internet as a source of
information may be costly. Another interesting issue is that the Internet is likely to
play a larger role as purchase decisions become more routine and there is less need
for sales assistance. Easy access to a wealth of online market information has made
the Internet an important resource for consumers (Bodur, Klein and Arora, 2015).
According to Ratchford, Talukdar and Lee (2001), Internet will be favored by younger
consumers who are already familiar with computers and have the most to gain from
investing in learning how to use the Internet.
Maity, Hsu and Pelton (2012) rightly emphasize that the Internet has gained
importance among today’s technology-savvy consumer and in effect the consumer
information search has been transformed by unprecedented access to technology –
enabled information platforms, an increasing number of consumers are engaging in
information search and transactions in the online environment. Bruggen et al. (2010)
note that for the consumers, user-generated content sites offers an unbiased sources
of information, although it introduces new risks, that is, content credibility and user
relevance (may no longer be completely customized). According to Moon and Frei
(2000), companies assume they should let their on-line customers help themselves to
whatever product or service they need. The problem is that when a company does
less, the customer ends up doing more – and most customers do not want to do
more. In many cases, self-service sites just leave customers frustrated and annoyed.
Kallweit, Spreer and Toporowski (2014) underline quite important issue
connecting with necessity of filtering the information – it is not important to provide a
high variety of information, but information with a high relevance for the customer’s
needs. Kirk, Chiagouris and Gopalakrishna (2012) claim that, besides the positive
effects of interactivity that characterizes the Internet sales and communication
channel, there is evidence that too much interactivity can result in so-called cognitive
overload. That is thus a counterweight in relation to the significant advantage of the
Internet consists in easy access to information (Zhang, 2009). Another issue
differentiating online and offline channel is that e-channel provides either limited or
no access to certain types of information. This has to do with the limitations of the
technology infrastructure. In effect, there are difficulties to mediate certain
information, such as information that stimulates the senses of touch and smell. The
social and visual stimulation that a physical store provides is also difficult to provide
by the e-channel (Wilkström, 2005).
In general, the quality of the information obtained in each channel refers to the
information content of these channels - content should be personalized, complete,
relevant, and easy to understand (Lee and Chen, 2014). According to Kim and Park
(2013) information quality refers to the latest, accurate, and complete information
provided to channel users. In relation to the online channel, it is an important
determinant of consumers’ trust in online environments, even more important for scommerce sites than for other types of e-commerce sites. Chang, Lee and Lai (2012)
note that the quality of information is one of the factors determining the quality of
service in a given channel (again research has focused on online channel). In turn,
Thaichon et al. (2014), have demonstrated that information quality directly influenced
customer commitment Kim and Niehm (2009). In our study we mean perceived
channel quality, which is – at the stage of searching for information about services –
formed by the prism of the quality of information provided by the channel.
It is apparent reference to the media richness theory. As Brunelle (2009)
claims, media richness refers to a medium’s ability to convey certain types of
information and is determined by its capacity for immediate feedback, the multiple
cues and senses involved, language variety, and personalization. Patricio, Fisk and
Cunha (2008) have made the channels classification (online, offline and phone)
taking into account three criteria: usefulness, efficiency, personal contact. According
to these researchers, phone channel falls between online channel and offline
channel: it is efficient, but not as much as online; it provides some personal contact,
but not as much as offline. Interestingly, if customers used only one service interface,
phone channel could be considered the one that offered the best balance between
efficiency and personal contact. However, from a multi interface perspective, the
value of phone channel to the overall service experience seems to be in question, as
it is not the best on any dimension. It appears, therefore, that the classification of the
media according to their information richness developed by Suh (1999) does not lose
its importance. Nowadays, in the era of mobile channel popularity, in the general
classification it is perceived as poorer than the Internet channel (Maity and Dass,
2014). However, it should be pointed out that the assessment of media richness is
not so unequivocal, the degree of media richness may not only vary across channels,
but also within a specific channel (Maity and Dass, 2014) – eg. a mobile channel with
audio/video capabilities is richer than a mobile channel with text-only capabilities, and
online channel can be rich if it will be supplemented in the chat (Kwak, 2012).
Another implication is that richer channels generally involve a higher cost to the user,
but positively affect the perceived quality of the information received through as well
as perceived purchase risk (Lo and Lee, 2008). Nevertheless, media richness is a
key channel characteristic that affects consumer behavior (Maity and Dass, 2014).
Another factor taken by us into consideration in the study is perceived risk of
using a specific channel to search for information. The entire research was consisted
of four purchasing process stages – searching for information, purchase, posttransaction service and resignation. In this article, our attention is focused on the first
phase. Interestingly, according to Kollmann, Kuckertz and Kayser (2012) risk
aversion is not significant in the case of searching for information. The explanation is
quite clear - the mere information search does usually not involve the disclosure of
personal data, so customers do not show significant risk aversion in this regard.
Undoubtedly far more space is devoted to the analysis of perceived risk on the
purchase stage. However, our research focuses on different generations of
consumers, hence the interest in possible differences in risk perception of searching
for information about services in the three analyzed channels. In our opinion, at the
stage of searching for information it may occur risk arising from consumer awareness
about a particular channel and the opportunities to use any information about
consumer behavior (eg. even browsing the shop offers without logging leaves a
trace, and provides to seller information about the potential client).
And finally, the last factor that shapes the intention to use the channel as a
source of information about services - perceived cost. The issue of the cost of using a
particular channel has been consciously limited by us to non-financial elements –
unpleasant sensations, time and effort. It is available a broad literature on the
importance of price, but we were interested in the results of Monga and Saini (2009).
These authors have shown why and how search occurs differently in time than in
money, they have found that the willingness to search is less influenced by search
incentives when people search by spending time rather than money. Their
experiments have shown that in the currency of money, a decrease in search costs
has a consistent and significant effect on the willingness to search. But if the currency
of search is time, a decrease in search costs has a significantly weaker effect in the
context of search. Generally, people are more likely to ignore information about costs
and payoffs when the currency is time rather than money (Monga and Saini, 2009).
3. Research Methodology
3.1. Population and Sampling
The research sample was determined by quota-random method, quotas due to
age and gender and the nature of the place of residence (city provincial, city other
than provincial, village) –the structure of sample was preserved at the regional level.
This means that we set the number of interviews for each province proportional to the
share of the population, then we set the number of interviews to conduct in the type
locality (city provincial, city other than provincial, village), the number of interviews
also reflected the number of inhabitants for the province. Then, from the address
database starting points were drawn, their number was due to the number of
interviews to conduct. The interviewer guided the drawn address and chose
household using random route method. The interviewer's task was to visit in every
second premises. If it was closed, the interviewer went to a sequential number, and if
he had there an interview, he walked two numbers on to the next premises. Within
the drawn household there was invited to interview a person who has recently
celebrated a birthday, and as the realization of interviews and pursue its attempts, a
person belonging to the quotas (by gender –the structure of the Polish and by age –
structure imposed because of the research objectives–generations comparison).
The study was conducted in September-November 2015 on a group of 1103
respondents including 357 from a Baby boomers generation, 390 from the X
generation and 356 from the Y generation. Due to the distinct differences between
the extreme generations (Baby boomer and Y generation) they have been presented
in the article. Consumers belonging to the X generation possess certain
characteristics of both the older and younger generation - hence the lack of such
visible characteristics in channel choice as a source of information about services.
CAPI (computer assisted personal interview) method was used with a
standardized questionnaire. Questions about the perception of channel
characteristics have been scaled using a seven-point Likert scale (1 – strongly
disagree; 7 – strongly agree). The characteristics of the study sample are presented
in Table 1.
Gender
Generation
Table 1. Characteristics of the study sample
Number of
Characteristics
respondents
Female
565
Male
538
Baby boomers (1946-1964)
357
X (1965-1980)
390
Y (1981-1996)
356
Percentage of sample
51.2
48.8
32.4
35.4
32.3
Full time employed
Part time employed
Entrepreneur
Not employed
Retired
Other
1
2
3
4
5 or more
Employment
status
Number of people
in the household
608
82
74
123
185
51
108
329
323
245
98
55.1
7.4
6.7
11.2
16.8
2.8
9.8
29.8
29.3
22.2
8.8
3.2. Conceptual model and hypotheses
We formulate the following hypotheses – they are illustrated in Figure 1, which shows
the structure of the conceptual model:
H1: The perceived channel quality is the main predictor of intention to use a specific
channel as a source of information about services.
H2: The intention to use a specific channel at the stage of searching for information is
indirectly shaped by the perceived media richness of channel.
H3: Generation Y has the higher (than the Baby Boomers) intention to use the phone
channel and the online channel as a source of information about services (this
hypothesis was verified outside the model)
H4: Costs of channel using have negative effect on intention to use channel as a
source of information about services.
H5: The perceived risk has no effect on intention to use channel as a source of
information about services.
Media
richness
H2
H1
Quality
H4
Intention to use
Costs
H5
Risk
Figure 1. The proposed research model
Construct
Perceived
media
richness (MR)
Adapted from:
(Lee, Cheung
and Chen,
2007)
Table 2. Selected measures of contracts’ reliability and validity
Cronbach’s alfa
AVE
Items
BB
Y
BB
Y
MR1: While searching for
information about services in
online channel I can get an
immediate feedback
MR2: Contact in online
0.820
0.745
0.65
0.62
channel fits to search for
CR
BB
Y
0.84
0.83
information about services
MR3: While searching for
information about services in
online channel I can get
multiple types of information
Perceived
Q1: Using the online channel, I
channel
can quickly obtain the
quality (Q)
necessary information
Adapted from: Q2: I have no problems with
(Chang, Lee
obtaining information about
0.882
and Lai, 2012) services in online channel.
Q3: Using the online channel, I
can obtain current information
on services.
Perceived risk R1: Searching for information
(R)
about services in online
Adapted from: channel may lead to adverse
(Maity, Hsu
consequences.
and Pelton,
R2: While searching for
2012),
information about services in
0.685
(Park, Gunn,
online channel I am afraid to
Han, 2012)
disclosure of personal data.
R3: Searching for information
about services in online
channel is risky.
Perceived
C1: Searching for information
costs(C)
in person in online channel
Adapted from: exposes me to the unpleasant
(Maity and
sensations.
Dass, 2014)
C2: Searching for information
0.784
in person in online channel
takes a long time.
C3: Searching for information
in person in online channel
requires from me a lot of effort.
Intention to
IU1: There is a good chance
use (IU)
that I will use the online
Adapted from: channel to search for
(Roschk,
information about service.
Muller and
IU2: Most likely I will use
Gelbrich,
online channel to search for
0.956
2013)
information about services.
IU3: I intend to use in the
future online channel in order
search for information about
services.
Note: BB – Baby boomers generation, Y – Y generation.
0.811
0.78
0.65
0.91
0.85
0.731
0.56
0.65
0.79
0.84
0.827
0.70
0.71
0.87
0.88
0.897
0.90
0.83
0.96
0.94
4. Results
All independent and dependent latent variables were included in multifactorial
confirmatory factor analysis (CFA) in AMOS 21.0. Satisfactory adjustment measures
obtained leading analysis separately for different generations. The CFA models were
performed using asymptotically distribution-free estimation. Estimates presented
relate standardized regression weights.
The first analysis concerns Gen Y and the perception by respondents the
Internet channel at the pre-purchasing stage – searching for information about
services. We support H1 – quality affected intention of usage Internet channel (β =
.75, p < 0.001). The greatest impact on the estimate has a statement Q2. Media
richness has indirect positive influence on intention of usage, which confirms H2.
Media richness explains many as 97% of the variation in channel quality. Costs of
channel using has no impact on intention of usage Internet channel which denies H3.
Consumers do not perceive as the real cost situation when they do not spent money.
Non-financal costs are not important. H5 surprisingly has been denied – the
perceived risk has a negative impact on the intention to use the Internet to search for
information (β = -.26, p < 0.05). Probably young consumers are aware leaving
information about themselves on the Internet every time they use it. Featured model
(Figure 2) explains 58% of the dependent variable (the intention to use online
channel).
Media
richness
(H2) .99***
(H1) .75***
Quality
2
(R =.97)
Intention to use
2
(R =.58)
(H4) .20 ns
Costs
.91
(H5) -.26*
Risk
Note: ns – not significant; *** - p < 0.001, * - p < 0.05.
Model fit – CMIN/DF 2.31, GFI .916, AGFI .881, RMSEA .061 (LO 90 .050 – HI 90 .072), PCLOSE
.053
Figure 2. Summary of research results (Y generation)
Table 3. Y generation
Hypothesis
H1
H2
Quality IU
MR Quality
p-value
Estimates
0.001
0.001
.754
.985
Acceptance or
rejection
3
3
H4
H5
0.106
0.034
Costs IU
Risk IU
.203
- .262
The second analysis concerns the Baby boomers generation and the
perception by respondents of the Internet channel at the pre-purchase stage –
searching for information about services. CFA confirm hypothesis H1, H2 and H5.
Risk is not important for Baby boomers when people search for information about
services. It may mean that older customers do not realize how non-annymous they
are in the internet environment. Within this factor Baby boomers generation differs
from the Y generation, that is more aware of the potential threats of online search.
Featured model (Figure 3) explains 52% of the dependent variable (the intention to
use online channel).
Media
richness
(H2) .984***
(H1) .716***
Quality
2
R =.97
Intention to use
2
R =.52
(H4) .177 ns
Costs
.82
(H5) -.166 ns
Risk
Note: ns – not significant; *** - p < 0.001.
Model fit – CMIN/DF 1.81, GFI .844, AGFI .780, RMSEA .048 (LO 90 .035 – HI 90 .060), PCLOSE
.611.
Figure 3. Summary of research results (Baby boomers generation)
Table 4. Baby boomers generation
Hypothesis
H1
H2
H4
H5
Quality IU
MR Quality
Costs IU
Risk IU
p-value
Estimates
0.001
0.001
0.084
0.109
.716
.984
.177
-.166
Acceptance or
rejection
✔
✔
✔
The study shows significant differences in the assessment of the perceived
media richness and intention to use marketing channels by Baby boomers generation
as well as Y generation. The evaluation of media richness of both generations are
similar in case for offline channel. The same applies intention to use this channel
(Table 5). T test for independent samples confirms the lack of statistically significant
differences in the assessments of offline channel. While, there are clear differences
in latent variables (media richness and intention to use) assessing made by both
generations. Y generation, in comparison to Baby boomer generation, perceive the
online channel and phone channel as richer in information. Consequently, there is
higher intention to use these channels by younger generation. The observed
differences in the means (Table 5) are statistically significant at p < 0.001, what
confirms H3.
Table 5. Characteristics of generations – basic descriptive statistics
Construct
Channel
Statement
Mean
MR1
5.82
Off-line
MR2
5.69
MR3
5.71
MR1
4.62
Media
Telephone
MR2
4.84
Richness
MR3
4.80
MR1
4.07
Internet
MR2
4.45
Baby
MR3
4.39
Boomers
IU1
5.50
generation
Off-line
IU2
5.48
IU3
5.51
IU1
4.36
Intention of
Telephone
IU2
4.38
usage
IU3
4.41
IU1
3.86
Internet
IU2
3.81
IU3
3.83
MR1
5.83
Off-line
MR2
5.77
MR3
5.70
MR1
5.54
Media
Telephone
MR2
5.58
Richness
MR3
5.44
MR1
5.58
Y generation
Internet
MR2
5.79
MR3
5.56
IU1
5.49
Off-line
IU2
5.52
IU3
5.53
IU1
5.23
Intention to
Telephone
IU2
5.35
use
IU3
5.34
IU1
5.38
Internet
IU2
5.48
IU3
5.53
Generation
5. Conclusion and Managerial Implications
SD
1.15
1.06
1.04
1.63
1.47
1.39
1.57
1.50
1.38
1.16
1.22
1.22
1.51
1.59
1.63
1.62
1.71
1.73
1.05
1.00
1.02
1.26
1.17
1.20
1.21
1.05
1.16
1.12
1.13
1.09
1.32
1.31
1.31
1.26
1.25
1.15
The contributions of this paper are manifold. First, this paper indicates the birth
of a completely new category of consumers – Y generation. Our research shows that
young consumers have totally different characteristics – they have much greater
potential to use online and phone channels, they are also aware of the risk of
searching for information in online channels. What is more, generation Y has a
greater intention to use online and phone channel, so it can be said that for them
offline channel may not exist – all the necessary information can be retrieved by them
without direct contact with the supplier in a stationary store. Second, this paper
investigates the effect of media richness on consumer perception of channel quality
and indirectly on intentions to use specific channel as a source of information about
services. Therefore an important recommendation to suppliers is to provide the best
media richness in channels other than offline. The aim should be to ensure in online
channel and phone channel such media richness components, as: immediate
reaction, language diversity, personalization and the number of provided tips (Lee,
Cheung and Chen, 2007). Third, we have confirmed in our study differences in the
perception of the cost, depending on form they take – monetary or non-monetary
(Monga and Saini, 2009). What consumers have to sacrifice to get information about
services, but what is not denominated in money is not regarded by them as a cost.
The cost, which is the main component of the give aspects (Carlson, O’Cass and
Ahrholdt, 2015) and in effect it reduces the perceived value. It turns out that
consumers underestimate the cost of wasted time that could otherwise exploit.
Finally, this research opens up many new avenues for future research.
6. Limitations and Future Research Implications
Although important issues emerged from our work, there are some limitations
which should be taken into account, these also suggest directions for further
research. The first limitation concerns the model – in order to confirm such a
importance of the media richness analyze of the impact of this factor against others
channel characteristics, which are not included in our model, should be done.
Another limit concerns the number of investigated channels. In our opinion, the
importance of mobile applications justifies their inclusion in future analyzes as one of
the marketing channel. Since our study indicates similar perceptions of costs and
risks of searching for information, future works should focus on the relationships
between perceived risk and perceived costs of using marketing channels as a
sources of information. In omnichanneling theory a single marketing channel should
not create a sustainable competitive advantage, hence our inclusion in the latent
variable only component of non-financial costs. As it turns out, these costs are not
relevant to the consumer and in the evaluation are strongly correlated with the risk.
You may also find that at other stages of the service purchasing process, media
richness will not be so important, and part of its impact will be transferred to
importance of perceived cost and risk.
Acknowledgement
This publication was financed by the Polish Ministry of Science and Higher Education
through the grant of the National Science Centre, No. 2014/13/B/HS4/01612: Modeling of
service distribution in network economy.
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