The impact of e-retailer personality and website quality on online impulse buying

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113

97

The impact of e-retailer personality and website quality

on online impulse buying

Nguyen Le Thai Hoa^1*

¹Marketing lecturer in Saigon Technology University (STU), Ho Chi Minh City, Vietnam

^*Corresponding author: hoamai54@yahoo.com

ARTICLE INFO

ABSTRACT

DOI:10.46223/HCMCOUJS.

econ.en.11.2.1400.2021

The development of information technology and the

proliferation of e-commerce make online shopping more and more

popular. Recent studies indicate that in the modern world, most

shoppers purchased products spontaneously and highlighted the

necessity of in-depth understanding of impulse buying as an

emerging phenomenon in marketing literature. A large number of

studies focus on the factors effective on consumers’ impulse

buying in brick and mortar retailers but rare research investigate

these factors in online environment. There are two key

perspectives on the factors effective on impulse buying: a

customer’s inherent characters and his/her current state of mind.

Based on the self-congruity theory and latent state-trait theory, this

study considered this concept in two-sided approach: the state of

mind incurred in the shopping environment (website quality) and a

particular personal characteristics inherent to the individual

customer (e-retailer personality). This paper adapted the concept of

retail brand/store personality from brick and mortar context to

internet marketing by investigating the impact of e-retailer

personality on website quality and impulse buying. Data were

collected from 563 online shoppers in Vietnam by online survey

and analyzed with Structural Equation Model (SEM). The results

indicate some practical implications for website design and

enhancing impulsive buying.

Received: January 12^th, 2021

Revised: March 24^th, 2021

Accepted: April 01^st, 2021

Keywords:

e-retailer personality; online

impulse buying; retailing;

website quality

1. Introduction

The rapid growth of Internet and information technology facilitates consumers to shop

online much more easily than before. According to the report released by eConomy SEA 2019

by Google and Temasek (2019), in Vietnam, 39.9 million people make purchases online with an

average shopping value of $202, which jumped up 11.8% compared with the year of 2018. The

growth of e-commerce is approximately 81% and the internet penetration rate (68 million users)

in total retail sales of consumer goods exceeded 4.2%. This figure creates a big business

opportunity for online retailers to expand their business.

With the potential of e-commerce, more and more e-retailers participate and make the

market intensely competitive. Several e-retailers attempt to create a distinctive and engaging

website and improve their service to meet the ultimate consumers’ increasing demands. The

previous studies investigated the influence of factors on online buying behaviors.

Besides popular effective factors such as website investment (Schlosser, White, & Lloyd,

2006), website customer orientations (Poddar, Donthu, & Wei, 2009) and website quality

98

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113

(Akrimi & Khemakhem, 2014; Poddar et al., 2009; Turkyilmaz, Erdema, & Uslua, 2015), e-

retailer/website personality is complementary in recent research. Ailawadi and Keller (2004)

acknowledged that building a prominent brand personality would help corporates survive in

intensely competitive situations since most corporates tried to deliver similar products/services.

The e-retailing context, where the mature stage was approached, was highly relevant. The

expectations from online shoppers have been higher and higher; and satisfying online shoppers is

now more difficult than before. Leen, Ramayah, and Omar (2010) discovered that e-retailers

should design their websites to go beyond mere interface development and make an effort on

website personality. However, the prior studies mainly approached the e-retailer personality as a

uni-dimentional construct and the measurement scale was developed in an offline context. Some

studies also proved the positive relationship between e-retailer personality and customer

satisfaction (Akrimi & Khemakhem, 2014; Chen & Rodgers, 2006), perceptions of the quality

and value of the site (Chen & Rodgers, 2006; Poddar et al., 2009), customer-site relationships

and revisit intentions (Chen & Rodgers, 2006), customer trust (Leen et al., 2010), purchase

intention (Poddar et al., 2009), e-shopping site involvement and site attitudes (Shobeiri,

Mazaheri, & Laroche, 2015) and buying impulsiveness (Akram et al., 2017; Turkyilmaz et al.,

2015; Wells, Parboteeah, & Valacich, 2011).

Concerning impulsive buying, Floh and Madlberger (2013) asserted that most shoppers

purchased goods spontaneously in modern life and emphasized the necessity of profound

knowledge of impulse buying on the internet. Verhagen and Dolen (2011) also reported that

approximately 40% of online shopping transactions were regarded as impulse purchases,

whereas Wu, Chen, and Chiu (2016) demonstrated that 82% of consumers engaged in impulse

buying. In traditional retailing, impulse buying triggered 30 - 50% of all retail sales (Dawson &

Kim, 2010). Previous studies also showed that the impulse buying phenomenon was adapted for

most kinds of goods, including expensive items (Rook & Fisher, 1995). As a result, retailers

concentrate on product displays, store designs, and package designs to attract shoppers’ attention

and boost their impulse buying.

Being a common concept in consumer behavior marketing, buying impulsiveness has

been strongly confirmed in traditional retailing but still disputed in e-commerce. The existing

research analyzed the factors effective on online impulse buying from two key perspectives: the

state of mind incurred in the shopping environment (website quality) (Rook, 1987) and particular

personal characteristic inherent to the individual customer (Wells et al., 2011). Especially,

various environmental cues (Parboteeah, Valacich, & Wells, 2009); website quality (Akram et

al., 2017; Turkyilmaz et al., 2015; Wells et al., 2011) have been observed as consumer’s state of

mind to influence on impulse buying. Alternatively, human characteristics (inherent

impulsiveness) were investigated in the effect of online buying (Zhang, Prybutok, & Koh, 2006).

Consequently, some scholars argued that these studies using only this dichotomy of trait versus

state might result in an oversimplified, one-sided view of the behavior (Akram et al., 2017;

Turkyilmaz et al., 2015). Further to this research trend to generalize marketing literature, both

views (state and trait) have also been taken into consideration. Website quality was assumed to

be a state (external factor) while e-retailer personality was deemed a trait (internal factor).

Therefore, this study contributed to this stream of research by investigating the impact of each

dimension in the multi-dimensional construct of an e-retailer’s personality and website quality

on impulse buying.

The research results may bring some practical implications for managers in raising their

awareness of e-retailers’ personalities compared with competitors and defining target segments and

positioning strategies to enhance the competitive advantages for their sites. Providing solutions to

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113

99

these issues would help to boost impulse buying online. The remainder of the research is followed

by a literature review, proposed model, research method, data analysis results and discussion,

theoretical and practical implications, and future research recommendations.

2. Literature review and research model

2.1. Foundational theories

2.1.1. Self-congruity theory

The human self is constructed with four dimensions: actual self, ideal self, social self, and

social-ideal self (Johar & Sirgy, 1991). The theory indicates that greater congruity between an

individual’s actual and ideal self and the characteristics that describe the brand creates a greater

preference for that brand.

2.1.2. Latent state-trait theory

According to this theory, human behavior depends on individuals’ characteristics,

environmental conditions (states), and the interaction between these two factors. In this regard,

impulse buying behavior is a dependent variable, and e-retailer personality and website quality

are independent variables.

2.2. E-retailer personality

The general definition of brand personality in brick-and-mortar context has been agreed in

scholar community in marketing literature, defined by Aaker (1997, p. 347) as “a set of human

characteristics associated with a brand.” After that, various studies made efforts to extend the

construct of brand personality to retailing market. Zentes, Dirk, and Hanna (2008, p. 167) defined

that: “A retail brand was referred as a group of the retailers’ outlets which carry a unique name,

symbol, logo or combination thereof” whereas Ailawadi and Keller (2004, p. 332) stated that: “A

retail brand identifies the goods and services of a retailer and differentiates them from those of

competitors.” Actually, the concept of store (retail brand) personality was firstly identified by

Martineau (1958, p. 47) defined as “the way in which store is defined in the shopper’s mind partly

by its functional qualities and partly an aura of psychological attributes.” However, four

dimensions of store personality in Martineau’s seminal research, including symbols and colors,

layout and architecture, sales personnel, and advertising, were actually similar to those of the

current store image concept. Thus, D’Astous and Levesque (2003, pp. 456-457) differentiated store

personality from store image when they stated that: “Whereas store image is mental representation

that encompasses all dimensions that are associated with a store (value for money, product

selection, quality of service, ect.), store personality is restricted to those mental dimensions that

correspond to human traits.” For example, product quality plays an important role of forming

overall store image but it is definitely not a personality trait. D’Astous and Levesque (2003, p. 457)

defined that: “store personality is the mental representation of a store on dimensions that typically

capture an individual’s personality.” Recently, in department store context in India, Das, Datta,

and Guin (2012, p. 98) argued that: “store personality was a consumer’s perception of the human

personality traits attributed to a store”.

In an online context, Park, Choi, and Kim (2005, p. 7) for the ﬁrst time deﬁned the

concept of e-brand personality as the “brand personality of an online product or service, usually

represented by a website.” Chen and Rodgers (2006, pp. 49-50) asserted that the creation of

website personality was sourced from not only on direct and indirect contacts (similar to the case

of human and brand personalities) but also the design of the site’s interfaces. Thus, e-retailer

personality was defined as “the set of traits encompassing human characteristics and

information technology features associated with an e-retailer.” Accordingly, Poddar et al. (2009,

100

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113

p. 442) also deﬁned e-retailer personality as “the mental representation of a web store on

dimensions that are similar to and reﬂect the dimensions of human personality.”

A retail brand/store personality is a multi-dimensional construct with different

dimensions in various research contexts (D’Astous & Levesque, 2003; Willems, Swinner,

Janssens, & Brengman, 2011). As usual, product brand personality was mentioned into all

positive dimensions whereas a retail brand/store personality was referred to some negative ones

as well, for instance, the unpleasantness (D’Astous & Levesque, 2003), informality, ruthlessness

(Davies, Chun, Silva, & Roper, 2004), deceitfulness (Ambroise & Florence, 2010) and chaos

(Willems et al., 2011). The current research approached the e-retailer personality and then the

measurement scale developed by Chen and Rodgers (2006) was applied, including five

dimensions: intelligent, fun, candid, organized, and sincere. Among these five e-retailer

personality dimensions, three dimensions were deemed to be related to the Big Five human and

brand personality. Particularly, Sincere is the same as Agreeableness (human personality) and

Sincerity (brand personality) when capturing the idea of warmth and acceptance. The fun

dimension, which conveys the notion of sociability, energy and activity, is similar to

Extroversion (human) and Excitement (brand). Intelligent is correlated to Conscientiousness

(human) and Competence (brand) since it encapsulates responsibility, dependability and security.

The two remaining dimensions (Candid and organized) are different from the “Big Five” and

refers to perceived ease of use, one of the fundamental determinants of Technology Acceptance

Model (TAM) by Davis (1989).

2.3. Website quality

The prior studies recommended several websites attributes that may create its quality.

Website quality is the perceived overall quality of a website according to the customer’s viewpoint

(Poddar et al., 2009). Ranganathan and Ganapathy (2002) stated that design, privacy, information

content, and website security were four dimensions of website quality whereas Wolfinbarger and

Gilly (2003) only highlighted two dimensions: design and content which may enhance website

quality to attract more online shoppers. Loiacono, Watson, and Goodhue (2007) suggested four

distinct features of website quality which were generally used in several studies, including

usefulness, ease of use, entertainment, and complementary relationship. Recently, Wells et al.

(2011) generalized from different sources and recommended that website quality consisted of three

main dimensions: Security, navigability, and visual appeal. Even though the above research

considered website quality as a multi-dimensional construct, this paper approaches it as overall

quality in uni-dimensional construct following Yoo and Donthu (2001) research.

2.4. Impulse buying

Most previous studies on impulse buying have focused on the traditional shopping

environment (Jeffrey & Hodge, 2007). Actually, the studies on impulse buying were mentioned

from the 1950s. Most of these studies during this duration considered impulse buying as

“unplanned” purchases. However, Rook (1987, p. 191) pointed out that impulse buying implied a

narrower and more specific range of phenomena than unplanned purchasing did. He stated that:

“impulse buying occurs when consumers experience sudden, generally powerful and persistent

urge to buy something immediately.” According to Sharma, Sivakumaran, and Marshall (2010,

p. 4): “Impulse buying is a sudden, hedonically complex purchase behavior in which the rapidity

of the impulse purchase precludes any thoughtful, deliberate consideration of alternative or

future implications.” Consistent with the stated definitions above, Beatty and Ferrell (1998) have

provided a more extensive definition stating that impulse buying is considered to be a sudden

and immediate purchase with no pre-shopping intentions either to buy the specific product

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113 101

category or to fulfill a particular buying task. Impulse purchasing accounts for roughly 40% of

online expenditures (Verhagen & Dolen, 2011); as a result, exploration of this phenomenon and

its detailed drivers is essential.

2.5. Research model and hypothesis development

2.5.1. E-retailer personality and quality

Poddar et al. (2009) argued that consumers determined their perception of website quality

only after considering the site's personality. In other words, perceived quality is the function of

the way a website look likes and a website with positive personality dimensions would appear to

be high-quality site and vice versa. Thus, the first hypothesis is proposed as follows:

Hypothesis 1: The Intelligent (a), Fun (b), Sincere (c), Organized (d) and Candid (e) of e-

retailer personality have a positive impact on website quality

2.5.2. E-retailer personality and impulse buying

According to the self-congruity theory as well as the latent trait-state theory, previous

research has asserted that individual characteristics of human personality positively or negatively

influence impulse buying. Materialism is one of the most important traits effective on

impulsiveness (Richins & Dawson, 1992). Impulsiveness is another trait in online impulse

buying (Liu, Li, & Hu, 2013; Wells et al., 2011; Zhang et al., 2006). Turkyilmaz et al. (2015)

used a more comprehensive Big Five model to demonstrate the significant relationship between

personality traits and buying impulsiveness.

E-retailer personality refers to the mental representation of e-retailer dimensions that are

similar to and reflect those of human personality (Poddar et al., 2009). Therefore, the argument

leads us to formulate the following hypotheses:

Hypothesis 2: The Intelligent (a), Fun (b), Sincere (c), Organized (d) and Candid (e) of e-

retailer personality have a positive impact on impulse buying

2.5.3. Website quality and impulse buying

The positive relationship between website quality and impulse buying has been

determined through various existing research. Firstly, Childers, Carr, Peck, and Carsoni (2001)

proved “web atmospherics” in the online scenarios to trigger impulse buying. This term refers to

website design features like graphics, frameworks, layout, navigational structure, search engine

configuration, text color and fonts, hypertext links, “one-click” purchase button or quick

payment, and media dimensions. Wolfinbarger and Gilly (2003) also revealed that a well-

designed interface increased the probability of customers’ impulse buying. Afterward, some

recent studies added more features for website quality and verified similar relations. Wells et al.

(2011) confirmed that the website’s features such as transaction safety, visual appeal, and

navigation directly affectedimpulse purchases. The research result from Turkyilmaz et al. (2015)

also revealed that three out of four dimensions of website quality (ease of use, usefulness, and

entertainment) had a positive relationship with impulse buying Akram et al. (2017) also

demonstrated that the overall quality based on four dimensions was positively related to impulse

buying. The third hypothesis is then suggested as follows:

Hypothesis 3: The website quality has a positive impact on impulse buying

Based on the above research hypothesis and arguments, this study proposes the research

model as follows (Figure 1):

102

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113

Figure 1. Proposed research model

3. Research methodology

3.1. Sampling and data collection

The paper used the mixed research methodology starting with the qualitative to explore

and adjust indicators for measurement scales and investigating the hypotheses with the

quantitative research. The qualitative research used a focus group with n = 11 (participants

included 04 students, 03 office staffs and 04 managers; their ages from 20 to 42; their gender of

05 males and 06 females; their online shopping experience of 01 year minimum). The

quantitative research applied the survey of consumers who usually shop online by convenient

sampling from three sources: (i) the direct survey from the classes, shopping centers, cinemas;

(ii) sending emails to existing data of Lazada, Tiki, Shopee customers; and (iii) sharing the link

google form on social media. A sample size of 700 respondents was targeted and a total of 608

questionnaires were completed, with a return rate of 86.9%. Invalid and uncompleted

questionnaires were rejected, resulting in 563 valid answer sheets. Respondents were instructed

to think about the e-retailer from which they had most recently purchased and evaluated that e-

retailer in mind. To better help respondents recall the past shopping experience, the e-retailers’

name and the recent time of the purchase were asked at the beginning of the questionnaire. Such

method allowed obtaining the actual online purchase experiences.

The research sample was balanced with the gender (female: 48.5% and male: 51.5%).

The age group of 18 - 25 years old accounted for the most with 44.4%, the next was 26 - 35 and

36 - 45 years old with 21.3% and 19.5 % accordingly. This implied that the youth liked to

purchase products online. Almost 85% of respondents had a monthly income below VND 15

million and their educational level mainly was college and university degree holders (53.2%).

Respondents’ online purchases included the following: (1) food & drink (24%); (2) mommy &

baby (20%); (3) home living & lifestyle (17%); (4) fashion apparel (14%); (5) electronics (11%);

(6) travel (7%); (7) online music (4%); and (8) others (3%).

3.2. Measurement scales

The personality of the e-retailer’s website was measured as a multi-dimensional construct

developed by Chen and Rodgers (2006), including five dimensions: Intelligent, fun, organized,

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113 103

candid, and sincere. The scale of Intelligent has 08 items (from IN01 to IN08); Fun has 06 items

(FU01 to FU06); Organized has 04 items (OR01 to OR04), and both Candid and Sincere have 02

items and is added one more new item from the focus group. Website quality has reduced 08

items inherited from Loiacono et al. (2007) and Turkyilmaz et al. (2015), and impulse buying

has 05 items from the research of Rook and Fisher (1995); Wells et al. (2011) (See more details

in the appendix). Items were measured by seven-point Likert scales with anchors of “not at all

descriptive of this website” and “completely descriptive of this website.”

4. Research results

4.1. Testing the scales of constructs by EFA and Cronbach Alpha

Firstly, the Cronbach alpha reliability was tested and an item of web quality (WQ01) was

deleted since the total-item correlation was below 0.30. The results asserted that the Cronbach

alpha of seven constructs was higher than 0.70 (the lowest was 0.893 and the highest was 0.989),

total-item correlations were above 0.30. Thus, the reliability of constructs was obtained

(Nunnally, 1978). The research results from exploratory factor analysis after eliminating three

items with loading factors below 0.5 or the difference between two loading factors less than 0.3,

including IN05, IN08, and FU02 indicated that KMO index was 0.890 (above 0.50). Barlett

testing was statistically significant at the level of less than 0.05 to meet the requirement for EFA

analysis. EFA result revealed that all the scales of constructs met the requirement the number of

factors extracted (07 factors were extracted as per the proposed research model), the cumulative

extracted variance equaled to 77.694 % (above 50%), eigenvalues were 11.152, 3.818, 3.605,

2.438, 1.835, 1.432 and 1.361 (more than 01). The loading factors were very high (the highest

was CA01 = 0.894 and the lowest was FU01 = 0.625) (see Table 1). Therefore, six constructs in

the research model with 33 items were extracted to meet the requirement of convergent validity

and discriminant validity (Hair, Black, Babin, & Anderson, 2010).

Table 1

EFA and Cronbach Alpha results

Constructs

7

6

No

Items

1

2

Fun

3

4

5

Impulse

buying

Website

quality

Intelligent

Organized Candid

Sincere

1

2

3

4

5

6

7

8

9

IN02

IN03

IN01

IN07

IN04

IN06

FU05

FU06

FU03

.826

.807

.773

.772

.753

.710

.859

.850

.827

.635

10 FU04

104

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113

Constructs

6

7

No

Items

1

2

Fun

3

4

5

Website

quality

Impulse

buying

Intelligent

Organized Candid

Sincere

11 FU01

12 OR02

13 OR01

14 OR03

15 OR04

16 CA01

17 CA03

18 CA02

19 SI03

.625

.845

.822

.816

.795

.894

.891

.889

.886

.884

.879

20 SI02

21 SI01

22 WQ02

23 WQ08

24 WQ07

25 WQ04

26 WQ06

27 WQ05

28 WQ03

29 IB02

30 IB03

31 IB04

32 IB05

33 IB01

.860

.818

.801

.794

.764

.738

.720

.851

.844

.747

.736

.725

Cronbach

Alpha

.893

.908

.948

.941

.989

.912

.899

Source: Results from data analysis

4.2. CFA analysis for the full measurement model

Seven first-order constructs, including Intelligent, Fun, Organized, Candid, Sincere,

Website Quality, and Impulse Buying were evaluated in full measurement model by Confirmed

Factor Analysis (CFA) with 231 degrees of freedom.

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113 105

Unidimensionality: To improve the good fit for the model, some items with high

modification index were deleted one by one (IN01, IN03, FUN03, FU05, OR04, WQ02, WQ03,

IB03 and IB05). The unidimensionality was then satisfied and CFA results proved the good fit

model with: Chi-square χ²/df = 900.277; d/f = 231; p-value = 0.000; CMIN/df = 3.897 (within 02

to 05); GFI = 0.887; TLI = 0.944; CFI = 0.934 (above 0.9); RMSEA = 0.072 (below 0.08).

Convergent validity was acceptable when both loading factors (standardized estimate)

and AVE were greater than 0.50 (Hair et al., 2010). The analysis results showed that all the

loading factors were higher than 0.50 (Lowest: WQ = 0.65 and highest: SI03 = 0.99) and

significant the level of 0.50. Therefore, all constructs obtained convergent validity (see Figure 2).

Composite reliability and average variance extracted: Applying the formula calculating

composite reliability ρ_c(Jöreskog, 1971, p. 111) and variance extracted ρ_vc(Fornell & Larcker,

1981)¹, the results were shown in Table 2. Seven constructs met the requirement of Composite

Reliability (CR) greater than 0.7 and variance extracted (AVE) greater than 0.5 (50%) (Bagozzi

& Yi, 1988; Hair et al., 2010).

Discriminant validity: The model has discriminant validity when the correlation between

two constructs is less than 01 (r < 1) or both AVEs of two constructs are higher than the square

correlation between two constructs (Steenkamp & Van Trijp, 1991). Table 2 indicated that all

root square AVEs were higher than the square correlation, the discriminant validity was

established.

Table 2

CR, AVE statistics and Correlation matrix (Fornell-Larcker, 1981)

Constructs

Intelligent

Fun

CR

AVE

INT

FUN

ORG

CAN

SIN

WSQ

IMB

0.843 0.578 (0.760)

0.808 0.684

0.838 0.632

0.942 0.729

0.909 0.746

0.572

0.298

0.388

0.348

(0.827)

0.532

0.229

0.559

Organized

Candid

(0.794)

0.349

0.525

(0.854)

0.371

Sincere

(0.864)

0.261

Website

quality

0.880 0.772

0.878 0.707

0.386

0.333

0.326

0.606

0.316

0.677

0.325

0.094

(0.879)

0.377

Impulse

buying

0.392

(0.841)

Note: The brackets () scores diagonal are the square root of AVEs of the individual constructs. Non-diagonal values

are cross construct squared correlations

Source: The result from data analysis

2

푝

푖=1

푝

푖=1

∑

2

∑

(

λi)

λi²

푝

1

ρ_c=

& ρ_vc=

∑

푝

푖=1

푝

푖=1

λi² +∑ (1− λi²)

푖=1

∑

(

λi) +∑ (1− λi²)

푖=1

106

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113

Figure 2. CFA result for full measurement model (Standardized estimate)

4.3. Hypotheses testing by SEM

The paper used a Structural Equation Modeling (SEM) technique to test eleven proposed

hypotheses. The SEM results showed that the model achieved a good fit: Chi-square = 900,277;

df = 231; p-value = 0.000; CMIN/df = 3.897; GFI = 0.887; TLI = 0.934; CFI = 0.944; RMSEA =

0.072 (see Figure 3). Estimated results in Table 3 indicated that seven out of eleven hypotheses

were statistically significant and supported with p-value < 0.05.

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113 107

Figure 3. SEM analysis result (standardized)

Table 3

Results of hypotheses testing

Relationships

Website quality  Intelligent

Website quality Fun

Est.

.182

.080

.092

.142

-.003

.021

.529

.521

-.288

-.055

.272

S. E

.049

.079

.035

.043

.041

.059

.099

.045

.053

.049

.060

CR

3.737

1.014

2.659

3.274

-.065

.357

P

Hypotheses

*** H1a: Supported

.311 H1b: rejected

.008 H1c: Supported

.001 H1d: Supported

.948 H1e: rejected

.721 H2a: rejected

*** H2b: Supported

*** H2c: Supported

*** H2d: Supported

.269 H2e: rejected

*** H3: Supported

Website quality Organized

Website quality Candid

Website quality  Sincere

Impulse buying  Intelligent

Impulse buying  Fun

5.355

11.623

-5.415

-1.106

4.491

Impulse buying  Organized

Impulse buying  Candid

Impulse buying  Sincere

Impulse buying  Website quality

Source: The result from data analysis

Furthermore, this research also conducted the durability and reliability of standardized

estimates in the research model by bootstrapping with the repeated sample N = 5,000. The results

stated that although there was bias but not so high and acceptable (from – 0.003 to 0.005), and

the CR less than 1.96. Thus, it could be concluded that the estimates were reliable.

108

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113

5. Result discussion and managerial implications

The first research result indicated that three of the dimensions of e-retailer’s personality

had a significant impact on website quality, including intelligent, organized, candid (H1a, H1c,

H1d are supported). Poddar et al. (2009) also showed similar results but utilized the different e-

retailer personality scales inherited from D’Astous and Levesque (2003). The rejected H1b was

surprising but reasonable in practice since the shoppers’ perception on website quality was not

based on the items such as colorful, flashy, and so on. Secondly, the H2b, H2c, H2d was

supported, implying that Fun, Organized, Candid significantly influenced impulse buying. This

result enhanced the statement from previous studies of Liu et al. (2013), Turkyilmaz et al.

(2015), Wells et al. (2011), Zhang et al. (2006) with a new perspective to consider e-retailer

personality as a multi-dimensional construct. Besides, two e-retailer personality “Intelligent” and

“Sincere” were found not significantly relatedto impulsive buying because an intelligent shopper

will plan carefully and thoughtfully before purchasing something, and he/she will not be affected

by other external factors to buy impulsively. A sincere person with item authentic and down-to-

earth just want to buy what he/she really feels necessary. Lastly, the supported H3 reconfirmed

the positive relationship between website quality and impulse buying, which complied with

Wells et al. (2011), Turkyilmaz et al. (2015), and Akram et al. (2017). The research results

contribute a theoretical implication on the research model of the relationships among e-retailer

personality, website quality, and impulse buying in an emerging market in Asia.

Based on the research findings, some managerial implications are suggested for

marketing managers as follows:

One of the recent branding trends is to attribute human personality to product/retailer

brands. This research result revealed that e-retailer website personality had an impact on both

website quality and impulse buying. As a result, the retail management should determine their

personality in the segmentation, targeting, and positioning process to be distinct from their

competitors. Online customers will find themselves when surfing on websites with a similar

personality. An integrated marketing and communication plan should be adjusted accordingly to

direct the expected character for websites such as proficient, sophisticated, effective, systematic

(Intelligent), engaging, exciting, or vital (Fun), and so on. Besides, the website design should

also be reconstructed to be suitable with its personality.

Since website quality is an important determinant of online impulse buying. Therefore, e-

retailers should concentrate on enhancing the website quality by designing friendly user-

interface, product categories with attractive and detailed information, flexible and traceable

navigational structure along with highly productive search engine, adding more visual appeal,

emotional appeal and innovative features, and minimizing response time and the various gate of

payment with high security.

To sum up, e-retailers should pay much attention to improving website quality as well as

enhancing website personality in order to level up the buying impulsiveness of online shoppers.

Limitations and future research directions

The research sampling executed by the convenience method made the representative for

total population limited. Future research should overcome this limitation by quota sampling. A

wide variety of products was applied in this research; a specific product category should be

studied separately to see any difference on the purpose of recommending more accurate

managerial implications. Lastly, the website quality in this study was approached as a uni-

dimensional construct. The future research should take into consideration its four dimensions:

Ease of use, usefulness, entertainment, and complimentary relationship (Loiacono et al., 2007).

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113 109

References

Aaker, J. L. (1997). Dimensions of brand personality. Journal of Marketing Research, 34(3),

347-356.

Ailawadi, K. L., & Keller, K. L. (2004). Understanding retail branding: Conceptual insights and

research priorities. Journal of Retailing, 80(4), 331-342.

Akram, U., Hui, P., Khan, M. K., Tanveer, Y., Mehmood, K., & Ahmad, W. (2017). How

website quality affects online impulse buying: Moderating effects of sales promotion and

credit card use. Asia Pacific Journal of Marketing and Logistics, 30(1), 235-256.

doi:10.1108/APJML-04-2017-0073

Akrimi, Y., & Khemakhem, R. (2014). An analysis of perceived usability, perceived interactivity

and website personality and their effects on consumer satisfaction. International Journal of

Management Excellence, 2(3), 227-236.

Ambroise, L., & Florence, P. V. (2010). The brand personality metaphor and inter-product

stability of a specific parameter. Research Applied Market; 25(2), 3-28.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the

Academy of Marketing Science, 16(1), 74-94.

Beatty, S. E., & Ferrell, M. E. (1998). Impulse buying: Modeling its precursors. Journal of

Marketing, 74(2), 169-191.

Chen, Q., & Rodgers, S. (2006). Development of an instrument to measure web site personality.

Journal of Interactive Advertising, 7(1), 33-46.

Childers, T. L., Carr, C. L., Peck, J., & Carsoni, S. (2001). Hedonic and utilitarian motivations

for online retail shopping behavior. Journal of Retailing, 77(4), 511-535.

D’Astous, A., & Levesque, M. (2003). A scale for measuring store personality. Psychology &

Marketing, 20(5), 455-469.

Das, G., Datta, B., & Guin, K. K. (2012). From brands in general to retail brands: A review and

future agenda for brand personality measurement. The Marketing Review, 12(1), 91-106.

Davies, G., Chun, R., Silva, R. D., & Roper, S. (2004). Corporate character scale to assess

employee and customer views of organization reputation. Corporate Reputation Review,

7(2), 125-144.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of

information technology. MIS Quarterly, 13(3), 319-340.

Dawson, S., & Kim, M. (2010). Cues on apparel websites that trigger impulse purchases. Journal

of Fashion Marketing and Management, 14(2), 230-246.

Elliott, M. T., & Speck, P. S. (2005). Factors that affect attitude toward a retail website. Journal

of Marketing Theory and Practice, 13(1), 40-51.

Floh, A., & Madlberger, M. (2013). The role of atmospheric cues in online impulse buying

behavior. Electric Commerce Resaerch Application, 12(6), 425-439.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobserved

variables and measurement error. Journal of Marketing and Research, 18(1), 39-50.

110

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113

Google & Temasek. (2019). E-conomy SEA 2019: Swipe up and to the right - South East Asia $

100 billion internet economy. Retrieved December 5, 2020, from

https://www.thinkwithgoogle.com/intl/en-apac/consumer-insights/consumer-trends/e-

conomy-sea-2019-swipe-up-and-to-the-right-southeast-asias-100-billion-internet-economy/

Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (7th ed.). New

York, NY: Prentice Hall Inc.

Jeffrey, S. A., & Hodge, R. (2007). Factors influencing impulse buying during an online

purchase. Electronic Commerce Research, 7(1), 367-379.

Johar, J. S., & Sirgy, M. J. (1991). Value- expressive versus utilitarian advertising appeals:

When and why to use which appeal. Journal of Advertising, 20(3), 23-33.

Jöreskog, K. G. (1971). Analysing psychological data by structural analysis of covariance

matrices. In D. H. Krantz, R. Atkinson, R. D. Luce, & P. Supes (Eds.), Contemporary

developments in mathematical psychology (pp. 1-56). San Francisco, US: University of

Illinois Press.

Leen, J., Ramayah, T., & Omar, A. (2010). The impact of website personality on consumers’

initial trust towards online retailing websites. World Academy of Science, Engineering and

Technology, 4(6), 807-812.

Liu, Y., Li, H., & Hu, F. (2013). Website attributes in urging online impulse purchase: An

empirical investigation on consumer perceptions. Decision Support Systems, 55(3),

829-837.

Loiacono, E. T., Watson, R. T., & Goodhue, D. L. (2007). WebQual: An instrument for

consumer evaluation of web sites. International Journal of Electronic Commerce, 11(3),

51-87.

Martineau, P. (1958). The personality of the retail store. Harvard Business Review, 36, 47-55.

Nunnally, J. C. (1978). Psychometric theory. New York, NY: McGraw-Hill.

Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009). The influence of website characteristics

on a consumer's urge to buy impulsively. Information Systems Research, 20(1), 60-78.

Park, S. E., Choi, D., & Kim, J. (2005). Visualizing e-brand personality: exploratory studies on

visual attributes and e-brand personalities in Korea. International Journal of Human-

Computer Interaction, 19(1), 7-34.

Poddar, A., Donthu, N., & Wei, Y. (2009). Website customer orientations, website quality, and

purchase intentions: The role of website personality. Journal of Business Research, 62(4),

441-450.

Ranganathan, C., & Ganapathy, S. (2002). Key dimensions of B2C websites. Information and

Management, 39(2002), 457-465.

Richins, M. L., & Dawson, S. (1992). A consumer values orientation for materialism and its

measurement: Scale development and validation. Journal of Consumer Research, 19(3),

303-316.

Rook, D. W. (1987). The buying impulse. Journal of Consumer Research, 14(2), 189-199.

Rook, D. W., & Fisher, R. J. (1995). Normative influences on impulsive buying behavior.

Journal of Consumer Research, 22(3), 305-313.

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113 111

Sautter, P., Hyman, M. R., & Lukosius, V. (2004). E-Tail atmospherics: A critique of the

literature and model extension. Journal of Electronic Commerce, 5(1), 14-24.

Schlosser, A. E., White, T. B., & Lloyd, S. M. (2006). Converting website visitors into buyers:

How website investment increase consumer trusting beliefs and online purchase intensions.

Journal of Marketing, 70(2), 133-148.

Sharma, P., Sivakumaran, B., & Marshall, R. (2010). Impulse buying and variety seeking: A

trait-correlates perspective. Journal of Business Research, 63(3), 276-283.

Shobeiri, S., Laroche, M., & Mazaheri, E. (2013). Shaping e-retailer’s website personality: The

importance of experiential marketing. Journal of Retailing and Consumer Services, 20(1),

102-110.

Shobeiri, S., Mazaherib, E., & Laroche, M. (2015). How would the e-retailer’s website

personality impact customers’ attitudes toward the site? Journal of Marketing Theory and

Practice, 23(4), 388-401.

Steenkamp, J. B. E. M., & Van Trijp, H. C. M. (1991). The use of LISREL in validating

marketing constructs. International Journal of Research in Marketing, 8(4), 283-299.

Tariq, A., Wang, C., Tanveer, Y., Akram, U., & Muhammad, B. (2019). Online impulse buying

of organic food: A moderated (website personality) mediation (social appeal) process.

International Journal of Information Systems and Change Management, 11(1), 3-24.

Turkyilmaz, C. A., Erdema, S., & Uslua, A. (2015). The effects of personality traits and website

quality on online impulse buying. Procedia - Social and Behavioral Sciences, 175(2015),

98-105.

Verhagen, T., & Dolen, W. V. (2011). The influence of online store beliefs on consumer online

impulse buying: A model and empirical application. Information Management, 48(8), 320-

327.

Wells, J. D., Parboteeah, V., & Valacich, J. S. (2011). Online impulse buying: Understanding the

interplay between consumer impulsiveness and website quality. Journal of the Association

for Information Systems, 12(1), 32-56.

Willems, K., Swinner, G., Janssens, W., & Brengman, M. (2011). Fashion store personality:

Scale development and relation to self-congruity theory. Journal of Global Fashion

Marketing, 2(2), 55-65.

Wolfinbarger, M., & Gilly, M. C. (2003). ETAILQ: Dimensionalizing, measuring and predicting

etail quality. Journal of Marketing, 79(3), 183-198.

Wu, L., Chen, K., & Chiu, M. (2016). Defining key drivers of online impulse purchasing: A

perspective of both impulse shoppers and system users. International Journal of

Information Management, 36(3), 284-296.

Yoo, B., & Donthu, N. (2001). Developing a scale to measure the perceived quality of an internet

shopping site (SITEQUAL). Quarterly Journal of Electronic Commerce, 2(1), 31-46.

Zentes, J., Dirk, M., & Hanna, S. K. (2008). Brand personality of retailers: An analysis of its

applicability and its effect on store loyalty. The International Review of Retail,

Distribution and Consumer Research, 18(2), 167-184.

Zhang, X., Prybutok, V. R., & Koh, C. E. (2006). The role of impulsiveness in a tam-based online

purchasing behavior model. Information Resources Management Journal, 19(2), 54-68.

112

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113

APPENDIX A

Primary measurement scale

Item code Measurement scale

e-retailer brand/website personality

Intelligent

Reference

IN01

Searchable

Satisfying

Comprehensive

Knowlegdeable

Easy

IN02

IN03

IN04

Chen and Rodgers (2006)

IN05

IN06

competent

Fast

IN07

IN08

Concise

Fun

FU01

FU02

FU03

FU04

FU05

FU06

Organized

OR01

OR02

OR03

OR04

Candid

CA01

CA02

CA03

Sincere

SI01

Colourful

Attractive

Flashy

Chen and Rodgers (2006)

Action Packed

Interactive

Dynamic

Irritating >>> Calm

Discouraging >>> Encouraging

Intensive >>> well-organized

Cluttered >>> Considerable

Chen and Rodgers (2006)

Orderly

Straightforward

Chen and Rodgers (2006)

Authentic (New item from focus group)

Sincerely

SI02

Down-to-earth

SI03

Friendly (new item from focus group)

Nguyen Le Thai Hoa. HCMCOUJS-Economics and Business Administration, 11(2), 97-113 113

Website quality

WQ01

WQ02

WQ03

WQ04

WQ05

WQ06

WQ07

WQ08

This website is convenient to use

It is easy to search for information

This site is colourful

Yoo and Donthu (2001);

Loiacono et al. (2007);

Turkyilmaz et al. (2015).

This site is creative

This site shows good picture of the product

It is easy to access the results

The site has quick process

This site ensures me of security

Impulsive buying behavior

IB01

IB02

IB03

IB04

IB05

I often buy things spontaneously

"Just do it" describes the way I buy things

I often buy things without thinking

“I see it, I buy it" describes me

Rook and Fisher (1995);

Wells et al. (2011); Akram

et al. (2017).

"Buy now, think about it later" describes me

Source: Results from the qualitative research

Creative Commons Attribution-NonCommercial 4.0 International License.