Thái độ mua sắm của người tiêu dùng trong môi trường mua sắm đa kênh: Trường hợp nghiên cứu ở thành phố Đà Nẵng
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
CONSUMER PURCHASE ATTITUDE IN MULTI-CHANNEL SHOPPING
ENVIRONMENT - CASE IN DA NANG CITY
THÁI ĐỘ MUA SẮM CỦA NGƯỜI TIÊU DÙNG TRONG MÔI TRƯỜNG MUA SẮM ĐA
KÊNH: TRƯỜNG HỢP NGHIÊN CỨU Ở THÀNH PHỐ ĐÀ NẴNG
Dr. Ngô Thị Khuê Thư
Faculty of Marketing, University of Economics, The University of Danang, [email protected]
MBA. Trần Thị Khánh Hiền
University of Economics, The University of Danang, [email protected]
Abstract
More and more consumers prefer more channels and use different channels when they make the
purchase of goods and services. This study aims to understand the factors that influence shopping attitudes of
consumers on internet and stores in a multi-channel shopping environment. Questionnaires are sent directly to
the respondents. A number of 242 valid questionnaires were included in this study. The data is analyzed by the
process from factor analysis to reliability testing and regression analysis. The results show that the perceived
purchase risk, information availability and the service quality of the both internet and physical channel will
have a positive effect on the consumer purchase attitude of the Internet channel. These results not only help
managers to better understand what affects channel selection attitude in the procurement process, but also to
find a variety of consumer trend and then have suitable programs to attract and retain customers in the
decision-making process.
Keywords: Multi-channel, marketing channel, perceived channel attributes; purchase attitude.
Tóm tắt
Ngày càng nhiều người tiêu dùng thích nhiều kênh hơn và sử dụng các kênh khác nhau khi họ mua hàng
hóa và dịch vụ. Nghiên cứu này nhằm tìm hiểu các yếu tố ảnh hưởng đến thái độ mua sắm của người tiêu dùng
trên kênh internet và kênh cửa hàng trong môi trường mua sắm đa kênh. Bản câu hỏi được gửi trực tiếp đến
người trả lời. Một số lượng 242 bản câu hỏi hợp lệ đã được đưa vào nghiên cứu này. Dữ liệu được phân tích
theo quy trình từ phân tích nhân tố đến kiểm tra độ tin cậy và phân tích hồi quy. Kết quả cho thấy rủi ro mua
hàng, tính sẵn có của thông tin và chất lượng dịch vụ của kênh internet sẽ có tác động tích cực đến thái độ mua
hàng của người tiêu dùng đối với kênh Internet lẫn kênh cửa hàng. Những kết quả này không chỉ giúp các nhà
quản lý hiểu rõ hơn những gì ảnh hưởng đến thái độ lựa chọn kênh trong quá trình mua sắm mà còn tìm ra
nhiều xu hướng tiêu dùng và từ đó có các chương trình phù hợp để thu hút và giữ chân khách hàng trong quá
trình ra quyết định.
Từ khóa: Đa kênh, kênh marketing, thuộc tính kênh được nhận thức; thái độ mua hàng.
1. Introduction
With the appearance of Internet technology and increasingly fierce competitive market,
enterprises are using more marketing channels. On the other hand, more and more customers are
gradually turning into multi-channel shoppers, who use different channels during the shopping process
(Hsiao et al, 2012, Yu et al., 2011). Therefore, the use of multiple channels in the purchase process
makes it difficult for enterprises to connect, understand and satisfy customers. When consumers combine
multiple channels during the purchase process, their shopping behavior is complex and this affects
significantly the business profit. Attracting consumers using multiple channels is an important issue for
enterprises because they are usually more valuable than the research of consumer shopping behavior on a
channel, which help enterprises to have more opportunities to access customers, diversify marketing
methods to promote sales. Consequently, enterprises must manage efficiently multiple channels to
enhance the interactive ability with customer and increase revenue. Consumers can access and purchase
on various channels such as internet, shops, catalogs, telephones ... (Ansari, Mela and Neslin, 2008).
However, in this article, the authors focus on analyzing two channels that are the most popular and used
962
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
widely nowadays: internet and store. The story is to consider how to integrate the actual store and the
Internet for customer shopping happens. The previous studies often focus on consumer behavior on a
single channel in the other channel (Yu et al, 2011) for example, researching online shopping behavior,
or shopping at the store ... while studies on multiple channel behavior are limited, not mentioned much.
In fact, the current trend of shopping is that customers purchase two or more different channels in the
same product, for example, they can buy clothing online and even at the shop. There are a number of
factors that affect maintaining purchase on one channel with one item, and if they are satisfied with the
purchase on a particular channel, they will prefer to use that channel and maintain a purchase on that.
Therefore, shopping behavior on the channel is a key factor of channel choice and managers should
explore it more clearly. This study aims to find out the factors that have effect on purchase behavior on
internet channels and stores in the shopping multichannel environment, thus helping readers understand
clearly what impacts on channel selection behavior in the shopping process. In addition, this study also
helps managers find a variety of consumer trend, used for the program to attract and retain customers in
the shopping process, consumption.
2. Literature review
2.1. Multichannel marketing
Multi-channel marketing is a mode of which businesses make use with the aim to provide
information, goods and services as well as customer support via 2 or more channels (Rangaswamy et
Van Bruggen, 2005). Multi-channel marketing permits businesses to build a long-term customer
relationship by sufficiently giving information, products and support service via 2 or more channels
involved… Most of the recent studies have claimed that multi-channel marketing strategy helps
enhance the ability to provide products and services for customers, which helps satisfy customers and
even make them more loyal (Maity & Dass 2014).
According to Digital Marketing Company Evonomie (England), retailers have to assure that all
offline and online sales channels are integrated with each other. In spite of traditional trade channels,
web foundation or mobile apps, retailers also have to offer their customers a coherent experience that
is easy to browse and order a product. For instance, a purchaser knows some information about their
wanted product, then he or she accesses some retailers’ pages on Facebook. Hence, he or she clicks the
link attached to have access to that retailer’s website and afterwards orders online. Finally, that
purchaser chooses the methods of delivery near his or her house.
Consumers are able to approach products and services with channels. These days, there is a
host of different channels, including online channels such as websites, online newspapers, social media
and virtual apps accessed via computers, tablets as well as smartphones. Offline channels such as
shops, catalogs, sales clerks, customer care and the third party agent (Neslin & Shankar, 2009). In this
study, besides multi-channel trade is being mentioned, the author is showing his concerns about the
Internet and shops which are now the most ubiquitous channels in customers’ purchase.
Thus, as regards withstanding the recent multi-channel purchase tendency, retailers’ mission is
to be always ready to uninterruptedly interact with customers via all channels. Business men and
women have responsibility for providing their customers coherent information on products’ categories,
prices and discount policies. The multi-channel sale model allows its customers to make decisions on
time, locations, methods of payment and methods of delivery. Therefore, businesses need to
comprehend and establish more efficient marketing policies with the aim to suit customers’ needs.
2.2. Purchase Attitudes
Attitudes toward channels are defined as negative or positive perceptions of consumers for the
channel that they purchase (Fishbein et Ajzen, 1975). For example, a person can have or do not have a
preference for any brands, advertisements or any shops. Hence, the attitudes of the consumers are the
consumers’ perceptions of the channel to buy in a negative or positive way, which will regulate the
963
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
channel selection behavior. If a factor have a positive influence on the attitude channel, this will be the
basis for the consumers to select the channel to purchase, increase their channel loyalty and limit
channel switching (Pookulangara and Natesan 2010, Fishbein et Ajzen, 1975). Attitudes play an
important role in consumer decision-making as they are the best predictors of behavior. This is an
important basis for marketers to deliver effective marketing strategy that influences decision-making
and behavioral changes.
Customers can purchase goods in a variety of channels, and with some feelings of the channel,
they will lead to be increases their propensity to seek an alternative when they are not satisfied or
appropriate for their needs anymore. In this study, the attitude of shopping on the internet is deeply
understood when customers have some sense of the factors from the channel when making a purchase.
If they are satisfied and feel positive about a channel through a number of the channel’s elements, they
tend to select that channel for their purchase and vice versa.
2.3. Perceived Channel Attributes
In the decision-making process, there are many factors that affect the change of customer’s
choice of channels, forming two definition of loyalty behavior with channel and switching behavior.
The behavior of switching channel is claimed to be complicated because consumers use a variety of
channels in the buying process. The problems is that customers do not use distinct channels to buy
different products, they interact with each other across channels that means they purchase the same
product in two different channels. Customers convert channels based on the changing in demand and
they are influenced by a number of factors such as the perceived risk of the channel, reasonable price,
efforts in evaluating the choices, and waiting time.
If the usage of a channel for seeking information causes disadvantages, for instance they
waste a lot of time comparing the products together, making the suitable choice because of limited
information quantity and unconvincing, they will move to other channel. In addition, the purchase of
this channel has many obstacles such as time consuming order, payment problems, risk and price of
goods are not competitive they will have a tendency to use other channels to get relevant purchases.
The customer perceptions of channel affects channel buying behavior and drive their behavior,
the difference between internet channel and store channel (Wang et al, 2015). The questions are posed
that for the Internet channel, which factors affect to the purchase attitude of consumers? And these
factors affect positively or negatively; strongly or weakly to the consumer purchase attitude on the
Internet channel?
Generally, these factors are put into the following categories: Perceptions of search benefits,
Perceptions of the searching costs, Perceptions of shopping benefits, Perceptions of shopping costs.
Table 1: Perceived Channel Attributes affect consumer purchase attitude
Purchase
convenience/
benefits
Attribute
Author
Information Search Purchase
Purchase Price/shopping
Promotions
availability effort
effort
risk
costs
Verhoef et al., 2007
X
X
X
Moktarian
&Tang,2011
X
X
X
X
X
X
Wang et al., 2016
X
X
X
Fernández-Sabiote et
al., 2016
X
X
Wang et al., 2015
Jepsen,2007
X
X
X
X
X
X
X
X
964
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
3. Hypotheses development
Information availability
The first channel characteristic of search benefits from using a channel is information
availability. Perhaps, it could be the consumer’s perception of the quantity and quality of information
availability to evaluate the product or service in the specific channel (Jepsen 2007; Noble et al. 2005).
Or another idea from Alba et al., 1997, Hoque and Lohse, 1999), Ratchford et al., 2001 that this factor
is showed by the accessibility of information for consumers, and the ability to compare alternatives. If
the channel offers useful information, consumers will have positive attitudes of it and tend to increase
their search and purchase behaviors in the specific channel (Avery 1996; Duncan and Olshavsky
1982). Thus, this study proposes the following hypotheses:
H1a Information availability from the Internet channel has a positive effect on consumer
attitudes toward purchase on that channel.
H1b Information availability from the Physical channel has a positive effect on consumer
attitudes toward purchase on that channel.
Search effort
The perceived Search effort required (time, costs) and perceived difficulty for consumers to
gather information on the products and services (Baker et al. 2002; Ratchford et al. 2003; Kang, Herr
and Page 2003). It is important in consumers’ evaluations of whether to buy online or in-store (Gupta
et al. 2004). If consumers perceive the costs of search (including time and effort) as high, they will
avoid searching on that channel (Avery 1996; Punj and Stealin 1983). Thus, the following hypotheses
are proposed:
H2a Search effort from the Internet channel has a negative effect on consumer attitudes
toward purchase on that channel.
H2b Search effort from the Physical channel has a negative effect on consumer attitudes
toward purchase on that channel.
Purchase convenience
Consumers might be confused two benefits from using a channel for purchase: purchase
convenience and service quality. Three elements define purchase convenience: possession
convenience, transaction convenience, and time convenience (Forsythe et al. 2006; Schro¨der and
Zaharia 2008). Another hand, this factor could be the efficiency, ease and speed at which products can
be purchased (Mathwick, Malhotra and Rigdon, 2001, Messinger and Narasimhan, 1997). The
perceived ability to negotiate on price and other aspects of the products in a channel (Morton et al.,
2001). When consumers can conveniently buy products, they want they will tend to choose that
channel to make more purchases. Thus, this study proposes the following hypotheses:
H3a Purchase convenience from the Internet channel has a positive effect on consumer
attitudes toward purchase on that channel.
H3b Purchase convenience from the Physical channel has a positive effect on consumer
attitudes toward purchase on that channel.
Service quality
The perception on the delivered service in the channel during the purchase (Baker et al. 2002;
Homburg, Hoyer, and Fassnacht 2002). Service quality combines the service received during the
purchase process and the outcome; including exchange-refund policy for returns, helpfulness, product
warrantees, and post-purchase service (Chiang and Li 2010; Stanley and Wisner 2002. Service quality
is a key influence on consumers who are purchasing products and is an antecedent of consumer’s
965
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
positive attitudes (Kim et al. 2005; Parasuraman et al. 1988; Zeithaml et al. 1996), so they hold
increasing perceptions ofvalue in the retail context. Thus, the following hypotheses are proposed:
H4a Service quality from the Internet channel has a positive effect on consumer attitudes
toward purchase on that channel.
H4b Service quality from the Physical Channel has a positive effect on consumer attitudes
toward purchase on that channel.
Purchase effort
The channel characteristics of purchase effort and purchase risk associate with purchase costs of
using a channel. The perceived difficulty and time costs consumers experience when purchasing a
product using a specific channel (Baker et al. 2002 ; Bhatanagar and Ratchford 2004). A consumer may
not buy from the channel if they feel the purchase process hard, it’s so important to consider product
before buying them, but retailers need to show the advanced method for consumers clearly such as some
kind of modern technology way to get useful and quick deal. Besides, when consumers purchase online,
they cannot use their senses (e.g., touch) to evaluate a purchase then they spend more time (Gupta et al.
2004). As purchase effort increases, it has a negative effect on customer attitudes toward purchase
through a given channel (Forsythe and Shi 2003). Thus, the following hypotheses are proposed:
H5a Purchase effort from the Internet channel has a negative effect on consumer attitudes
toward purchase on that channel.
H5b Purchase effort from the Physical channel has a negative effect on consumer attitudes
toward purchase on that channel.
Purchase risk
The perceived uncertainty in buying products through a specific channel due to things such as
payment issues and lack of privacy (Hoffman, Novak, and Peralta 1999; McKnight et al. 2002;
Forsythe and Shi 2003). Consumers’ risk perceptions significantly influence decisions about whether
to purchase online or in a physical store (Burke 2002; Gupta et al. 2004; Reardon and McCorkle 2002;
Szymanski and Hise 2000). When they see something related to risks on product such as error items,
wrong payment… what they get directly that not seem like their thought or expectation. Some
problems could make consumers disappointed and they tend to move another channel to buy. Thus,
the following hypotheses are proposed:
H6a Purchase risk from the Internet has a negative effect on consumer attitudes toward
purchase on that channel.
H6b Purchase risk from the Physical has a negative effect on consumer attitudes toward
purchase on that channel.
4. Research methodology
4.1. Research model
From the analysis of the factors above and basing on the model of the Wang et al. in 2015 study
that are given common factors used to analyze their influences on the consumer purchase attitudes; the
authors put these factors into the following groups: perceptions of search benefits, perceptions of the
searching costs, perceptions of shopping benefits and perceptions of shopping costs.
966
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
Information availability
Figure 1: The proposed model
4.2. Data collection
In this study, 6 independent factors and 21 observed variables, using the exploratory factor
analysis according to EFA Gorsuch (1983) (cited by MacClall, 1999) with a minimum sample size of 5
* 21 = 105 samples. In addition, according to Tabachnick & Fidell (1991) for best regression analysis,
the sample size must satisfy the formula (according to Pham Anh Tuan, 2008) n ≥ 50+8p, n is the
sample size, p is the independent variable of the model, so the minimum sample size is 50 + 8 * 6 = 98
samples. To ensure reliable data requiring at least 200 samples, 300 questions will be generated. In
fact, the final sample of study is 242 respondents Da Nang city. The direct interview is the key way to
collect reliable and useful information.
To ensure that respondents could offer reliable responses to the measurement items, we asked
them to develop their answers based on products that they have searched and purchased through the
Internet channels in the last 3 months. Respondents may be also someone who haven’t yet purchase
but they can be in process of information search in the Internet.
5. Results
The survey was conducted in 2018 in Da Nang city. Table 3 reports a sample of 242
respondents representing multi-channel shoppers in Da Nang and provide demographic characteristics
of the respondents.
Table 2: Demographic Characteristics of Participants
Total
Overall Percent
Total
N=242
83
Overall Percent
Age
< 18
N=242 100.0
Gender
100.0
34.3
65.7
100.0
66.9
23.1
0.8
7
2.9
76.4
19.0
1.2
4
Male
18-25
185
46
3
Female
159
N=242
162
56
26-45
Career
46-65
Student
≥ 65
1
Officer
Education
Secondary I
Secondary II
N=242 100.0
Business
Others
2
5
62.1
6.2
4.5
81.0
5.8
4
16
6.6
15
Income
< 5 millions
5 -<10 millions
N=242
174
49
100.0
71.9
20.2
6.2
Technical/ College 11
University
Post Graduate
Others
196
14
1
10-<15 millions 15
15-<20 millions
>20 millions
3
1
1.2
0.4
967
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
The above table shows that the number of interviewees was female more than male, accounting
for 65.7%, and the age in group from 18-25 accounted for the majority of about 76.4%, mostly officers
and students with the income from 5 - 10 million is common. It can be seen that the trend of online
shopping is favored especially among young people, and the average living standard in Da Nang is 5-
10 millions. Enterprises need to understand the demographic characteristics in the locality to bring
products and services to meet the needs and customers.
5.1. Using Cronbach Alpha for scale reliability
The scale was evaluated through Cronbach Alpha coefficients in order to eliminate unreliable
variables before, the variables which have a Corrected Item- Total Correlation less than 0.3 will be
excluded and will select the scale which its credibility Alpha is more than 0.6, especially for the case
that the research concept is new to the respondents in the context of research (Nunnally, 1978;
Peterson, 1994; Slater, 1995).
• For Internet channel, the results of Alpha Cronbach reliability are following (items PC2 and
PR3 are eliminated)
• For Physical channel, the results of Alpha Cronbach reliability are following (items PEs3,
SQs3 are eliminated)
Table 3. Cronbach Alpha reliability (Internet channel and physical channel)
Internet Channel
Physical Channel
Cronbach
Alpha if
Item
Cronbach
Alpha if
Item
Corrected
Item- Total
Correlation
Corrected
Item- Total
Correlation
Items
Var.
Deleted
Deleted
Alpha= .760
.483 .745
(IA) Information availability. Alpha= .779
I can easily compare and select options of product X
from this channel
IA1
.589
.647
.612
.730
I can get useful information on product X from this
channel
IA2
.665
.705
.671
.644
.690
I can quickly find out information on product X from
this channel
IA3
.582
Alpha=.896
(SE) Search effort. Alpha= .709
It takes me much time to search for information on
product X from this channel
It takes me a lot of efforts to search for information on
product X from this channel
I need to obey certain process to search for information
on product X from this channel
SE1
.537
.574
.474
.607
.562
.686
.514
.514
SE2
SE3
(PC) Purchase convenience. Alpha=.689
Alpha=.896
I can buy product X at my available time from this
channel
I live more conveniently by buying product X from this
channel
I live more conveniently by buying product X from this
channel
PC1
.545
.545
.778
.867
.840
.849
PC3
.810
PC3
.801
(PR) Purchase risk Alpha=.791
Alpha=.704
.469
I think there are potential risks of getting the wrong
product X when buying from this channel
PR1
.666
.666
.679
.529
I think there are potential risks of incompletely
PR2
968
.593
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
examining the product quality when buying product X
from this channel
I think there are potential risks of incorrect payments
when buying product X from this channel
PR3
.508
.631
Alpha =.701
(PE) purchase effort. Alpha =.756
It costs me plenty time to buy product X from this
channel
It takes me a lot of efforts to buy product X from this
channel
PE1
.613
.641
.542
.542
PE2
.622
.525
.633
.741
PE3 It is hard to buy product X from this channel
Alpha= .664
(SQ) service quality. Alpha= .621
I can have good services for product X from this
channel
I can get great assistance when I want to purchase
product X from this channel
I can have flexible delivery options when buying
product X from this channel
I can easily complete my payment for product X from
this channel
SQ1
.325
.520
.416
.330
.305
.591
.493
.547
.589
.610
.407
.525
.623
.550
SQ2
SQ3
SQ4
.393
.663
.582
I can convenient return and exchange or receive refund
from this channel.
SQ5
.467
Alpha=.745
(PA) Purchase attitude. Alpha=.740
Overall, purchasing on this channel is satisfactory
PA1
.558
.665
.545
.691
Overall, purchasing on this channel is a smart decision
PA2
.592
.548
.626
.675
.614
.559
.614
.678
PA3 Overall, purchasing on this channel is enjoyable
5.2. Exploratoire Factor Analysis (EFA):
Factor analysis is a method of quantitative analysis which simplify a set of interdependent
observative variables into a fewer variables (called factors) so that they make more sense, but still
contains most significant contents of the original variables. (Hair et al, 1998)
The weighted variables (Factor loading) is less than 0.3 in the EFA will be excluded. We use
the principal factor components with varimax rotation until criticized elements «Eigenvalue» = 1. The
scale is accepted if the total variance extracted> = 50% (Gerbing & Anderson, 1988), with provided
that the KMO index> = 0.5. KMO is used to consider the appropriateness of the EFA, KMO ≤ 0.5 ≤ 1,
means the factor analysis appropriate.
Bartlett's Test: if this test is statistically significant (Sig <0.005), the observed variables are
correlated with each other in the overall.
EFA for independent variable:
• Physical channel: KMO value reached 0,675 > 0,5, this shows the data suitable for factor
analysis and Sig of Bartlett's Test = 0.000 <0.05, said that the observed variables are correlated
with each other.
The results of EFA for physical channel show that the variables of IAs3, IAs2, IAs1 correlated
with component 1, named «Infornation avaibility»; the variables of SQs2, SQs1, SQs4, SQs5
correlated with component 2, named «service quality»; the variables of PRs1, PRs2, PRs3 correlated
with component 3, named «purchase risk»; the variables of SEs1, SEs2, SEs3 correlated with
component 4, named «search effort»; the variables of PEs1, PEs2 correlated with component 5, named
969
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
«purchase effort» ; the variables of PCs1, PCs2, PCs3 correlated with component 6, named «purchase
convenience» in the physical channel.
• Internet channel: KMO value reached 0.708> 0.5, this shows the data suitable for factor
analysis; and Sig of Bartlett's Test = 0.000 <0.05, said that the observed variables are correlated
with each other on the whole. However, at the 1rst EFA, the difference between the two
loading factor is not be greater than 0.3; so the item SQ3, SQ4 and SQ1 will be disqualified.
We continue to analyze the 2nd EFA and 3rd EFA. The items IA1, IA3, PC3 are eliminated.
The results of EFA for Internet channel show that the variables of PR1, PR2, PC1, IA2 correlated
with component 1, named «the purchase risk and purchase convenience» (PR_PC_IA); the variables of
PE1, PE2, PE3 correlated with component 2, named «perceived effort»; the variables of SE1, SE2,
SE3 correlated with component 3, named «Search effort»; the variables of SQ2, SQ5 correlated with
component 4, named «service quality» in the Internet channel.
EFA for dependent variable:
Internet channel Physical channel
Component
.829
Component
.854
I- Overall, purchasing on this channel is satisfactory
I- Overall, purchasing on this channel is a clever decision .806
.813
I- Overall, purchasing on this channel is pleasant
Component- a1 components extracted.
.800
.807
Through the results of table above, we can see the variables of PA1, PA2, PA3 correlated with
component PA (purchase attitude) both in the internet channel and physical channel. These items are
retained in the subsequent analysis.
With the above results, compared with the proposed research model, the research model is
modified and the authors have assumptions about the factors affecting the purchase attitude on the
Internet channel as follows:
H1a
H1b
H2a
H2b
H3a
H3b
H4a
H4b
H5b
H6b
Risk purchase taking and purchase convenience from the Internet has a positive effect on consumer
attitudes toward purchase on that channel.
Information availability from the Physical channel has a positive effect on consumer attitudes toward
purchase on that channel.
Purchase effort from the Internet has a negative effect on consumer attitudes toward purchase on that
channel.
Purchase effort from the Physical channel has a negative effect on consumer attitudes toward purchase
on that channel.
Search effort from the Internet has a negative effect on consumer attitudes toward purchase on that
channel
Search effort from the Physical channel has a negative effect on consumer attitudes toward purchase
on that channel
Service quality from the Internet has a positive effect on consumer attitudes toward purchase on that
channel
Service quality from the Physical channel has a positive effect on consumer attitudes toward purchase
on that channel
Purchase convenience from the Physical channel has a positive effect on consumer attitudes toward
purchase on that channel.
Risk purchase taking from the Physical channel has a positive effect on consumer attitudes toward
purchase on that channel.
970
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
5.3. Linear regression analysis
The first step in conducting a linear regression analysis is to examine the linear correlation
between the dependent variable and each independent variable as well as between the independent
variables together. The results of correlation matrix between the variables show that the independent
variables are not perfectly correlated with each other, the correlation coefficient between the
independent variables are smaller than 1. Next, all variables are taken into the linear regression
analysis in order to examine the influence of the independent variables on the dependent variable.
For Physical channel, the value of Sig. F change is 0.000 <0.05; this means the independent
variables in the model have a relationship to the dependent variable “PAsnew”.
Table 4. Model Summary (physical channel)
Change Statistics
Adjusted R Std. Error of
Model
1
R
R Square
.342
R Square
Change
326
F
Sig. F
Change
.000b
Square
the Estimate
df1
6
df2
Change
22.067
.585a
.326
.67041
255
a. Predictors: (Constant) IAsnew, SQsnew, PRsnew, SEsnew, Pesnew
Coefficientsa (physical channel)
Unstandardized Coefficients
Std. Error
0,302
Standardized Coefficients
Beta
Model
T
Sig.
B
1
(Constant)
PCsnew
IAsnew
PRsnew
SQsnew
PEsnew
SEsnew
0,561
0,035
0,295
0,136
0,340
-0,014
0,061
1,856
0,065
0,042
0,054
0,046
0,060
0,049
0,043
0,043 0,833
0,308 5,455
0,160 2,942
0,324 5,619
-0,015 -0,278
0,079 1,426
0,406
0,000
0,004
0,000
0,781
0,155
a. Dependent Variable: REGR factor score 1 for analysis 2
Based on the results of the above table, the coefficient of R2= 0.326; means that 32.6% of the
variation in these factors: IAsnew, SQsnew, PRsnew, SEsnew, PEsnew, PCsnew affects PAsnew. The
results of the multiple regression analysis are summarized thus:
1. IAsnew factor reached a level of significance (ß=0.308 and p=0.000<0.05), which shows that
the information availibity from the physical channel will positively influence the consumer
purchase attitudes toward purchase on that channel. Therefore, Hypothesis H1b is accepted.
2. Service quality (SQsnew) factor reached a level of significance (ß=0.324 and p=0.000<0.05),
which means that service quality from the physical channel has a positive effect on consumer
attitudes toward purchase on that channel. Therefore, Hypothesis 4b is accepted.
3. PRssnew factor reached a level of significance (ß=0.160 and p=0.03<0.05), which means that
the purchase risk from the physical channel has a negative effect on consumer attitudes toward
purchase on that channel. Therefore, Hypothesis 6b is accepted.
These factors have an impact on the consumer purchase attitudes toward purchase on physical channel
in the same dimension, which the variable SQsnew is the most powerful. A simple regression was used
to test hypotheses H2b and H3b and H5b, the result indicates that the PEsnew and Sesnew, PCsnew
factor didn’t influence consumers purchase attitude toward purchase on channel (ß>0.05 and p=1.000).
Therefore, Hypothesis H2b and H3b and H5b is rejected.
For Internet channel, the value of Sig. F change is 0.000 <0.05; this indicates that variables
included are statistically significant at the 5% significance. Thus, the independent variables in the
model have a relationship to the dependent variable “PAnew”.
971
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
Table 5. Model Summary (internet channel)
Change Statistics
R
Adjusted R Std. Error of the
Model
1
R
F
Sig. F
Change
Square Square
Estimate
R Square Change
.260
df1
df2
Change
.522a .273 .260
.86154669
22.114
4
236 .000
a. Predictors: (Constant), SQ, SE, PE, PR_PC
Coefficientsa
Model
(Constant)
Unstandardized Coefficients Standardized Coefficients
t
Sig.
B
Std. Error
.055
Beta
.002
.395
-.029
.023
.341
.031
7.108
-.515
.408
.976
.000
.607
.684
.000
PR_PC_IA
.056
.056
.056
.056
.395
-.029
.023
.340
1
PE
SE
SQ
6.124
a. Dependent Variable: PAnew
The coefficient of R2= 0.273; means that 27.3% of the variation in these factors: PR_PC_IA,
PE, SE, SQ affects PAnew. The results of the multiple regression analysis are summarized thus:
4. Purchase risk taking and purchase convenience (PR_PC_IA) factor reached a level of
significance (ß=0.3950 and p=0.000<0.05). Therefore, Hypothesis H1a is accepted.
5. Service quality (SQ) factor reached a level of significance (ß=0.340 and p=0.000<0.05).
Therefore, Hypothesis 4a is accepted.
These factors have an impact on the consumer purchase attitudes toward purchase on Internet
channel in the same dimension, which the variable PR_PC_IA is the most powerful.
A simple regression was used to test hypotheses H2a and H3a, the result indicates that the PE
and SE factor didn’t influence consumers purchase attitude toward purchase on Internet (ß>0.05 and
p=1.000). Therefore, Hypothesis H2a and H3a is rejected.
Table 6: The overall results of research
SN
Sig.
Value
Hypothesis
Results
H1a Risk purchase taking and purchase convenience from the Internet has a positive 0.000
effect on consumer attitudes toward purchase on that channel.
Accepted
H1b Information availability from the Physical channel has a positive effect on 0.000
consumer attitudes toward purchase on that channel.
H2a Purchase effort from the Internet has a negative effect on consumer attitudes toward 0.607
purchase on that channel.
Accepted
Rejected
Rejected
Rejected
Rejected
Accepted
Accepted
H2b Purchase effort from the Physical channel has a negative effect on consumer 0.781
attitudes toward purchase on that channel.
H3a Search effort from the Internet has a negative effect on consumer attitudes toward 0.684
purchase on that channel
H3b Search effort from the Physical channel has a negative effect on consumer attitudes 0.155
toward purchase on that channel
H4a Service quality from the Internet has a positive effect on consumer attitudes toward 0.000
purchase on that channel
H4b Service quality from the Physical channel has a positive effect on consumer 0.000
attitudes toward purchase on that channel
972
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
H5b Purchase convenience from the Physical channel has a positive effect on consumer 0.406
attitudes toward purchase on that channel.
Rejected
Accepted
H6b Risk purchase taking from the Physical channel has a positive effect on consumer 0.004
attitudes toward purchase on that channel.
6. Discussion and Conclusion
Firstly, the author finds that the available information from a channel will have a significant
and positive impact on customers’ search behavior on both the Internet and physical channels (H1a,
H1b). This finding is fit with Chen and Wells (1999); Janda et al. (2002). The information provided by
the channel must be relevant, reliable, up-to-date and sufficient to meet the demands of consumers and
help them understand the product or service. When consumers think that the channel provides the
appropriate information, these positive perceptions encourage consumers to search and purchase more
in the future on this channel. Secondly, the quality of service is the factor affecting the purchase
behavior on both channels (H4a, H2b). Quality goods, good in-sale and after-sale service are the
driving force behind shopping on channels, so it affects to the buying behavior across channels
positively. Thirdly, an important channeling factor has a positive impact on consumer attitudes
towards shopping on both the Internet and store channel: risk acceptance (H1a and H6b). If customers
want to have a shopping experience with many new features and benefits of buying on the internet and
store, especially the young people will dare to take risk on purchasing in some cases. However, with
certain items ensuring safety, customers want to be consulted more effectively, and reduce the risks
encountered, compared to when they come to the shop. Fourthly, consumers’ shopping convenience
has a significant and positive impact on purchasing behavior on internet (H1a), but not significant in
the physical channel (H5b). This can be explained that customers are so familiar in shopping
traditional channel, stores are everywhere, diversity and popular, so they can visit the store at
appropriate times to buy an item they need with the support of the seller and a clear display, assistive
technology, so they will not take too much time to buy the product.
On the other hand, with two channels, searching and buying effort was removed from the
model, and it seems not to affect the multi-channel buying behavior. With the Internet, as analysis
above, the customer will not spend too much time and effort finding the product or information needed
thanks to a variety of assistant tools (Gupta et al., 2004; Lee and Kim 2008; Park and Kim 2003; Sen et
al. 2006). With stores, consumers find that shopping at stores is a traditional form, they are accustomed
to experiencing, walking around the booth to select and try on the product. For them, taking time and
effort to do that is a pleasure. Vietnamese consumers often spend their free time for shopping,
especially for family, group, and group shopping, and they prefer picking and choosing their favorite
items. Therefore, at stores, retailers arrange personnel, support systems to directly consult and take
care of customers at best. A Campbell study published in 2010 on the Wall Street Journal shows that
psychological marketing method, "Neoromarketing can make sales skyrocket by creating more
emotional responses at stores. and encourage consumers to pay more attention to the products. "Two
these factors do not change the shopping behavior significantly. The initial model in both channels has
six factors. This is the model learnt most from the latest Wang et al., 2016 study, and through the
qualitative research. However, after the analysis process, each channel has a difference in the new
model proposed. This change evaluated bases on the consuming behavior of the majority of people
surveyed in their local areas when shopping on both Internet and store channels.
Besides, this study has some limitations that could be addressed in the future research. Firstly,
the study has only been conducted in Danang but not in other areas yet, and the sample was mostly
aged from 18 to 45 years old, so that the size of representative consumer sample in Vietnam is not
large. The number of people of the survey sample is 242, which is still small. Secondly, this study
considers only two types of channels: Internet and physical channels, and only analyzes the features of
the channel that affect the channel behavior of multi-channel research shoppers. However, multi-
channel consumers’ behavior is complex. Future studies may extend the results of this study to
973
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
examine the effects of different channel types, different products, consumer demographics, consumers’
habits and preferences, the sensitivity of the consumer price and the type of channel (e.g., mobile
channel). This will improve our understanding of the multi-channel shopping behavior.
Finally, in the future, the author can use the second generation of statistical techniques in the
study, namely the Structural Equation Model (SEM), when the sample size is over 200 (Hair et al.,
2006). SEM can test more causal relationships at the same time and have more stringent analysis
results (Gefen et al. 2000). Anova analysis shows the difference in the degree of influence between
independent variables and the dependence of demographic features such as sex, age, occupation,
income. This brings much useful information for readers concerned about local demographics in order
to create sales strategies that are relevant to the target consumers.
REFERENCES
1. Ansari, A., Mela, C. F., & Neslin, S. A. (2008). Customer channel migration. Journal of Marketing
Research, 45(1), 60-76
2. Bhatnagar, A., & Ratchford, B. T. (2004). A model retail format competition for non- durable goods. International
Journal of Research inMarketing, 21, 39−59.
3. Fernández-Sabiote, E., & Román, S. (2016), “The multichannel customer’s service experience: building
satisfaction and trust”, Service Business, 1-23.
4. Fishbein, M., & Ajzen, I. (1975). Belief. Attitude, Intention and Behavior: An Introduction to Theory and
Research Reading, MA: Addison-Wesley, 6.
5. Hoffman, D. L., Novak, T. P., & Peralta, M. A. (1999). Information privacy in the marketspace: Implications
for the commercial uses of anonymity ofthe web. The Information Society, 15, 129-140.
6. Homburg, C., Hoyer, W. D., & Fassnacht, M. (2002). Service orientation of a retailer's business strategy:
Dimensions, antecedents, and performance outcomes. Journal of Marketing, 88, 86-101.
7. Jepsen, A. L. (2007). Factors affecting consumer use of the Internet for information search. Journal of
Interactive Marketing, 21(3), 21-34.
8. Kang, Y. S., Herr, P. M., & Page, C. M. (2003). Time and distance: Asymmetries in consumer trip knowledge
and judgments. Journal of Consumer Research, 30, 420-429.
9. Kim, Y. K., Park, S. H., & Pookulangara, S. (2005). Effects of multi-channel consumers' perceived retail
attributes on purchase intentions of clothing products. Journal of Marketing Channels, 12(4), 23-43.
10. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-
commerce: An integrative typology. Infor- mation Systems Research, 13, 334−359.
11. Macik, R., Mazurek, G., & Macik, D. (2012). Channel Characteristics' Influence on Physical vs. Virtual
Channel Choice for Information Search and Purchase-The Case of Polish Young Consumers. International
Journal of Cyber Society and Education, 5(1), 35.
12. Maity, M., & Dass, M. (2014). Consumer decision-making across modern and traditional channels: E-
commerce, m-commerce, in-store. Decision Support Systems, 61, 34-46.
13. Mokhtarian, P. L., & Tang, W. L. (2011), “Trivariate probit models of pre-purchase/purchase shopping
channel choice: clothing purchases in northern California, paper presented at International Choice
Modelling Conference 2011”, Oulton Hall (UK) 4–6 July 2011.
14. Neslin, S. A., & Shankar, V. (2009). Key issues in multichannel customer management: current knowledge
and future directions. Journal of interactive marketing, 23(1), 70-81.
15. Rangaswamy, A., & Van Bruggen, G. H. (2005). Opportunities and challenges in multichannel marketing:
An introduction to the special issue. Journal of Interactive Marketing, 19(2), 5-11.
16. Peterson R, 1994, “A Meta-analysis of Cronbach’s alpha Coefficient Alpha”, Journal of Consumer
Research.
17. Ratchford, B. T., Lee, M. S., & Talukdar, D. (2003). The impact of the internet on information search for
974
Kỷ yếu Hội thảo quốc tế “Thương mại và phân phối” lần 2 năm 2020
automobiles. Journal of Marketing Research, 40, 193-209.[37] Slater S, 1995, “Issues in Conducting
Marketing Strategy Research”, Journal of Strategic.
18. Verhoef, P. C., Neslin, S. A., & Vroomen, B. (2007), “Multichannel customer management: Understanding
the research-shopper phenomenon”, International Journal of Research in Marketing, 24(2), 129-148.
19. Wang, Y. M., Lin, H. H., Tai, W. C., & Fan, Y. L. (2015). Understanding multi-channel research shoppers:
an analysis of Internet and physical channels. Information Systems and e-Business Management, 1-25.
975
14 trang
23920
Free
Bạn đang xem tài liệu "Thái độ mua sắm của người tiêu dùng trong môi trường mua sắm đa kênh: Trường hợp nghiên cứu ở thành phố Đà Nẵng", để tải tài liệu gốc về máy hãy click vào nút Download ở trên
File đính kèm:
- thai_do_mua_sam_cua_nguoi_tieu_dung_trong_moi_truong_mua_sam.pdf
