Influence of On-line Brand Experience on purchase intention: An Empirical Study of non-standardized commodity

Chen Pang1,2 1 College of Fashion

Zhejiang Sci-tech University 2 School of Economics

Zhejiang University Hangzhou, China

e-mail:pangchen@foxmail.com

Xiaofen Ji College of Fashion

Zhejiang Sci-tech University Hangzhou, China

e-mail: xiaofenji@zstu.edu.com

Abstract— Since the explosion and popularity of Internet, virtual brand community has been serving as a new and important brand experience centre for consumers to acquire necessary brand and product knowledge. Based on the empirical literature and the nature of online shopping, this study proposed a series of research hypotheses and a theoretical model. Internet shoppers’ brand experience (sensory, affective, behavioral and intellectual factors), perceived risk (financial and product performance risk) and their relationship to purchase intention were introduced. The research was conducted by members of a brand virtual community through questionnaire toward non-standardized commodity. And research model was evaluated with structural equation modeling. Results of this empirical study indicated that sensory, affective and intellectual factors significantly influence perceived risk online. However, behavioral –oriented experience has no significant effect. Finally, conclusion and implications of these findings were also discussed.

Keywords-brand experience; perceived risk; purchase intention; online shopping

I. INTRODUCTION

Internet represents a new, highly technical channel and service to consumers. With the growth of on-line shopping market, increasing number of people prefer to purchasing in the web stores. Meanwhile many retail merchants have been changing the way they sell their goods, veering from the traditional shopping channels to e-commerce. They regard online sale as one supplementary route to increase their profits or just enhance brand reputation.

However, as far as non-standardized commodity is concerned, such as apparel, due to the inherent nature of the absence of sensory experience and physical inspection, some consumers are still reluctant to purchase on the Internet. Its quality may be examined only after touching and trying on. An overwhelming number of Internet users cite being unable to try on clothes before purchasing as one of the three biggest problems with shopping for clothes online. Thus non-standardized commodity possesses higher perceived risk than standardized one (software and books, for examples). Virtual community, a channel on-line customers exchange their shopping experiences, have enabled them to connect to

an ever increasing amount of information and knowledge with corporate, brands and products. Through mutual communicating in on-line communities, consumers are exposed to various specific brand-related stimuli, such as brand-identifying colors, shapes (Veryzer & Hutchinson 1998), design style, slogans and brand characters (Keller 1987). Participants who received sensory pleasure from the experiences in virtual communities were more willing to purchase products online. The knowledge acquired by brand experience can reduce the perceived risk when buying non-standardized commodity via Internet. Moreover, marketing practitioners have come to realize that understanding how consumers experience brands is critical for developing marketing strategies for goods and services (Brakus, Schmitt & Zarantonello, 2009).

From the perspective above, this paper proposed a series of research hypotheses and a theoretical model based on literature review. Following the way of “brand experience → perceived risk → buying intention”, the research was conducted with members of web community through questionnaire and analysis methods to test all the proposed research hypotheses and theoretical model. The purpose of this research is to explore the function mechanism of on-ling consumers’ brand experience on their willingness of buying non-standardized commodity in the virtual environment.

II. THEORETICAL BACKGROUND AND RESEARCH

HYPOTHESES

A. Brand experience and purchase behavior

Brand experience refer to subjective, internal consumer responses (sensations, feelings, and cognitions) and behavioral responses evoked by brand-related stimuli that are part of a brand’s design and identity, packaging, communications, and environments (Brakus， Schmitt & Zarantonello,2009). And the importance of brand experience on consumer behavior has been widely documented (Ha & Perks, 2005; Fullerton, 2005; Selnes, 1993). The consumers’ positive experiences with the brand may affect brand cognition, commitment and purchase intentions, and it also improves the brand’s reputation (Horppu, Kuivalainen, Tarkiainen & Ellonen, 2008). Therefore, we could

2010 Third International Symposium on Electronic Commerce and Security

978-0-7695-4219-5/10 $26.00 © 2010 IEEE

DOI 10.1109/ISECS.2010.75

311

reasonably assume that individuals’ behavior about brand experience can be seen as predictors of personal factors since it is considered as one of main influences shaping users’ behavior.

Importantly, positive and negative emotions may be not considered to directly affect actual buying behavior. Their effect on consumers’ purchasing intention is through the construct of perceived risk (NenaLim, 2003). Several researches suggest that, while risk propensity is relatively stable, it can differ by a decision context and be modified based on experience and knowledge about the situation. It is proposed that positive emotional experiences with brands decrease the degree of perceived risk while negative emotional experiences serve to increase the level of perceived risk. And six types of perceived risk have been identified: financial, product performance, social, psychological, physical, and time/convenience loss (S.M.Forsythe & Bo Shi, 2003). Our research focused on the financial risk and product performance risk. Financial risk is defined as a net loss of money to a customer (Sweeney, Soutar and Johnson, 1999). Financial risk is defined as a net loss of money to a customer. For example, unreliable vendors deliver unsatisfactory products or even fail to deliver products to consumers. Product performance risk refers to the loss incurred when a brand does not perform as expected (Horton, 1976).This dimension of perceived risk is similar to the usefulness or functionality of products.Therefore, the following hypothesis was developed:

Hypothesis 1: Shoppers’ positive brand experiences negatively affect their perceived financial risk with non-standardized commodity online.

Hypothesis 2: Shoppers’ positive brand experiences negatively affect their perceived product performance risk with non-standardized commodity online.

As part of the brand experience scale, Brakus, Schmitt & Zarantonello (2009) developed a 12-item scale to measure consumers’ brand experience with four dimensions: sensory, affective, behavioral, and intellectual factors. Therefore, the following hypotheses were developed:

Hypothesis 1a: Shoppers’ positive brand sensory experiences (BSE) negatively affect their perceived financial risk with non-standardized commodity online.

Hypothesis 1b: Shoppers’ positive brand affective experiences (BAE) negatively affect their perceived financial risk with non-standardized commodity online.

Hypothesis 1c: Shoppers’ positive brand behavioral experiences (BBE) negatively affect their perceived financial risk with non-standardized commodity online.

Hypothesis 1d: Shoppers’ positive brand intellectual experiences (BIE) negatively affect their perceived financial risk with non-standardized commodity online.

Hypothesis 2a: Shoppers’ positive brand sensory experiences (BSE) negatively affect their perceived product performance risk with non-standardized commodity online.

Hypothesis 2b: Shoppers’ positive brand affective experiences (BAE) negatively affect their perceived product performance risk with non-standardized commodity online.

Hypothesis 2c: Shoppers’ positive brand behavioral experiences (BBE) negatively affect their perceived product performance risk with non-standardized commodity online.

Hypothesis 2d: Shoppers’ positive brand intellectual experiences (BIE) negatively affect their perceived product performance risk with non-standardized commodity online.

B. Perceived risk and purchase intention

The theory of perceived risk has been utilized to explain consumers’ behavior. Perceived risk thus can be considered a function of the uncertainty about the potential outcomes of a behavior and the possible unpleasantness of these outcomes. It represents consumer uncertainty about loss or gain in a particular transaction (Murray, 1991). On-line shopping has been not an unfamiliar shopping mode with most Internet users. However, in-home shopping may still be considered as a higher-risk decision for non-standardized commodity in comparison with standardized one for following reasons: (1) lack of opportunity to examine products prior to a purchase; (2) frequent suspicion of reputation and ethics of certain brands and operations. Perceived risk is powerful at explaining consumer’s behavior because consumers are more often motivated to avoid mistakes than to maximize utility in purchasing (Forsythe & Bo Shi, 2003).

Both two performance risk may result from a poor product choice due to the shoppers’ inability to accurately judge the quality of the product online. Customers may be reluctant to choose non-standardized goods difficult to describe their colors, materials and outlines on the Internet context. Therefore, the following hypotheses were developed:

Hypothesis 3a: The higher the perceived level of financial risk (PFR) toward the brand, the lower the intention to purchasing this brand online (PI).

Hypothesis 3b: The higher the perceived level of product performance risk (PPR) toward the brand, the lower the intention to purchasing this brand online (PI).

Research model (Figure 1) can be seen below:

Figure 1. Research model

PFR

PPR

BSE

BAE

BBE

BIE

PI

H1a

H2a

H1b

H2b

H1c

H2c

H1d

H2d

H3b

H3a

312

III. METHODOLOGY

A. Sample and procedure

In order to target online consumers, an online community-based survey was employed. The survey learning website was designed with high usability to increase response rate and accuracy. 127 returned valid questionnaires were initially screened for usability and reliability from 146 responses. We adopted apparel as the typical non-standardized commodity. To minimize data entry error, all data were entered twice and checked for consistency. Detailed descriptive statistics relating to the respondents’ characteristics are shown in Table 1.

Independent-sample T test and analyses of variances were used to test demographic difference between Internet apparel purchasers and non-purchasers. Results indicated that Internet apparel purchasers were more likely to be female, young, students and aged from 18 to 24. Traditionally, Internet shoppers have been well-educated and wealthy men. Present we can see the changing demographics among Internet shoppers suggest that this group is moving from elite to mainstream.

TABLE I. PROFILE OF RESPONDENTS

Measure Item Percentage (%)

Male 40.25 Gender Female 59.75 Below 18 9.83 18-24 40.71 25-30 28.42 30-40 17.20

Age

Over 40 3.84 Less than 1000 20.12 1000-2000 31.80 2000-3000 17.97 3000-4000 14.13

Individual Income per month (RMB) Over 4000 15.98

Student 36.87 Clerical employee 31.33 government employee

4.30

Education facility employees

7.22

Occupation

Etc. 20.28

B. Measurement

Table 1 outlines scales developed to operationalized research constructs.

TABLE II. SOURCE OF SCALES

Variables Source/reference α BSE 0.83 BAE 0.81 BBE 0.76 BIE

Brakus,Schmitt &

Zarantonello(2009) 0.79

PFR -- PPR

Forsythe & Bo Shi(2003) --

PI Tony Ahn et al(2004) 0.92

Each of the variables shown in Figure 1 was measured with multiple items derived from prior literature and modified to fit the context of online learning community. Each Scale’s Cronbach’s α was also listed. Respondents were asked to indicate agreement with each statement in a measure using a five-point Likert-type scale (1,strongly disagree; 2, disagree; 3, neutral; 4, agree; 5,strongly agree). The measures related to each construct then were assessed using respondent perceptions.

IV. ANALYSIS AND RESULTS

There were two parts of our analysis. First, validity and reliability of the measurement model were tested by factor analysis and Cronbach’s α. Second, the causal structure of the proposed research model was tested using structured equation modeling (SEM).

A. Measurement model

All items should be purified before factor analysis. Items with the value of Corrected item-total correlation (CITC) below 0.5 were dropped if scales’ Cronbach’s α increased. Principal components analysis with varimax rotation was used to test the efficiency and validity of SEM. With KMO value all above 0.7 and a significant value for Bartlett’s test, the data was adequate for factor analysis. The ultimate results of descriptive statistics and reliability analysis were shown in Table 2.

TABLE III. RESULTS OF DESCRIPTIVE STATISTICS AND RELIABILITY

ANALYSIS

Variables M Std. CITC α BSE 3.145 0.756 ≧0.61 0.73

BAE 3.267 0.772 ≧0.67 0.79

BBE 3.212 0.834 ≧0.69 0.76

BIE 3.233 0.826 ≧0.73 0.83

PFR 3.212 0.834 ≧0.70 0.82

PPR 3.012 0.821 ≧0.75 0.87

PI 3.531 0.832 ≧0.73 0.86

B. Structural model

The causal structure of the research model was tested using SEM. 127 samples of data from respondents were analyzed using Amos 7.0 to test the SEM. The goodness-of-fit indices for this model were calculated.

All measures satisfied the criteria of good fit. The results also showed that not all of the parameters were significant in our study. Two proposed hypotheses was not supported (H1c and H2c), while the rest were significant (p<0.01). Therefore, we deleted H1c and H2c, then retested the compared model. The goodness-of-fit indices for this model were calculated again. The goodness-of-fit indices for this model were shown in Table 4 and the test demonstrated reasonable fit between the data and the proposed structure model. All measures satisfied the criteria of good fit, except for the Chi-square. However, this statistic is sensitive to large sample sizes and tends to reject the model for trivial discrepancies between a

313

model and data, resulting in significant Chi-square values. Because the data was based on a relatively large sample, χ2/df instead of χ2 should be considered. Therefore, results of the analysis indicated a good fit between the structural model and the data.

The compared model yielded a χ2/df value of 1.537.The GFI, AGFI, IFI, CFI and RMSEA were 0.897, 0.886, 0.901, 0.900 and 0.065.

TABLE IV. FIT MEASURES FOR THE STRUCTURAL MODEL

Fit indicators Criteria Results

χ2

/df <5 1.537

GFI ≧0.85 (Bentler,1992) 0.897

AGFI ≧0.85 (Bentler,1992) 0.886

IFI ≧0.9 (Bentler,1992) 0.901

CFI ≧0.9 (Bentler,1992) 0.900

RMSEA <0.1(Steiger,1990) 0.065

All measures of compared model satisfied the criteria of

good fit. Furthermore, all of the parameters in compared model were significant (p<0.01). Therefore, compared model, see Figure 2, was our study’s final model. And following conclusions were all based on this final model.

Figure 2. Compared model

Figure 2 illustrates the results of the structural model and details of the statistics. Results of the analysis supported H1a, H2a, H1b, H2b, H1d, H2d, H3a and H3b. Brand sensory, affective and intellectual experiences have significant effects on perceived risk. However, the effect of brand behavioral experience on both perceived financial and product performance risk was not supported by the data. Thus, hypothesis H1c and H2c was rejected.

V. CONCLUSION AND IMPLICATION

This study explored the mechanism of on-ling consumers’ brand experience on their purchase intention on non-standardized commodity online. Our work generated following research conclusions: mechanism of on-ling consumers’ brand experience on their willingness of buying non-standardized commodity.

Through checking the original model and compared model, we found that the proposed model was valid in explaining and predicting consumer behaviors in the e-commerce context. Brand sensory, affective and intellectual experiences

have significant effects on perceived risk. Sensory experience plays a critical role in guiding online consumers’ behavior via reducing their perceived risk. Shoppers’ brand behavioral experience was weakly

related to both perceived financial and product performance risk compared with other three dimensions. A possible explanation for this finding is that activities of brand experience online may be the short-term behavior due to the lower cost for their transfer among different brands. Generally, if you have a good opinion of a brand, you will dispel your mind of apprehensions when buying it online requiring no continuing experience behavior. But then, when consumers dislike a certain brands, they may not continue to conduct actual brand experience behavior.

The empirical findings presented in this study also provide helpful market strategies for e-stores and brand virtual communities.

First, emotional satisfaction may be more crucial than behavioral interactivity. Therefore, relation marketing can be an effective strategy to utilize in the Internet environment.

Second, it is better for enterprises to carry interactive activities based on the living pattern of their target consumers to enhance their positive affective experience.

Third, due to the crucial role of brand sensory experience in purchasing behavior online, e-browsers who failed to receive desired sensory pleasure from the product and/or display were reluctant to choose the certain brands, especially in the initial stage. E-retailers in China may be able to identify their weaknesses and improve the inadequacy in online visual merchandising strategies in the following aspects. They could provide a higher number of human models to exhibit garments, if presenting 3-D manual or automatic rotations is difficult to achieve to some extent. Models should feature both front and back views. Many e-retailers in the US have offered customers this type of visual inspection. E-merchants also can enhance their brand communities’ environment by adding more product images that are not currently featured on their website, including color chips and the addition of multiple product images.

The selected influences for this study may be limited to represent the most appropriate reflection of the whole situation, as this study was limited to the four dimensions of brand experience. Due to the rudimentary level of the development of the scale of brand experience, some variables may not be used. However, findings of this exploratory study still contribute to the understanding of current practices of brand experience related to non-standardized products' perceived risk under Chinese Internet background. Internet retailers in China should realize the significant importance of brand experience under the Internet

PFR

PPR

BSE

BAE

BIE

PI 0.19**

0.20**

0.25**

0.27**

0.15**

0.17**

0.36**

0.31**

** Significant at 0.01;

314

environment, especially the sensory and affective factors. In addition, a contribution of this study is to provide preliminary information and suggestions helpful for virtual community to take primary steps forward in the correct direction.

REFERENCES

[1] Veryzer, Robert W. and J. Wesley Hutchinson, “The Influence of

Unity and Prototypicality on Aesthetic Responses to New Product Designs,” Journal of Consumer Research, vol. 24, pp. 374–394, March. 1998.

[2] Keller, Kevin L., “Memory Factors in Advertising: The Effects of Advertising Retrieval Cues on Brand Evaluations,” Journal of Consumer Research, vol.14, pp316–333, December. 1987.

[3] Brakus, Schmitt & Zarantonello, “Brand Experience:What Is It? How Is It Measured? Does It Affect Loyalty?” Journal of Marketing, vol. 73, pp.52-68, May. 2009.

[4] Ha,H.-Y.andPerks,H.,“Effects of consumer perceptions of brand experience on the web: brand familiarity, satisfaction and brand trust”, Journal of Consumer Behaviour,Vol.4, No.6,pp.438-52, 2005.

[5] Fullerton,G.,“The impact of brand commitment on loyalty to retail service brand”, Canadian Journal of Administrative Sciences,Vol.22, No.2, pp.97-110, 2005.

[6] Selnes,F.,“An examination of the product performance on brand reputation, satisfaction and loyalty”, European Journal of Marketing,Vol.27, No.9, pp.19-35, 1993.

[7] Marianne Horppu, Olli Kuivalainen, Anssi Tarkiainen and Hanna-Kaisa Ellonen, “Online satisfaction, trust and loyalty, and the impact of the offline parent brand,” Journal of Product & Brand Management, Vol.17, No.6, pp.403-413, 2008.

[8] NenaLim, “Consumers’ perceived risk: sources versus consequences,” Electronic Commerce Research and Applications, Vol.2, pp.216-228, 2003.

[9] S.M.Forsythe & Bo Shi, “Consumer patronage and risk perceptions in Internet shopping,” Journal of Business Research, vol. 56, pp. 867–875, 2003.

[10] Sweeney JC, SoutarGN, JohnsonLW., “The role of perceived risk in the quality–value relationship: a study in a retail environment, “Journal of Retailing, Vol.75, No.1, pp. 77–105, 1999.

[11] Horton RL., “The structure of decision risk: some further progress,” Journal of Academic Market Science, vol. 4, pp. 694–706, 1976.

[12] Murray KB., “A test of services marketing theory: consumer information acquisition activities,” Journal of Marketing, vol. 55, No.1, pp. 10–25, Jan.1991.

[13] Tony Ahn , Seewon Ryu, and Ingoo Han., “The impact of the online and offline features on the user acceptance of Internet shopping malls,” Electronic Commerce Research and Applications, vol. 3, pp. 405–420, 2004.

[14] Segars, A. & Grover, V., “ Re-examining perceived ease of use and usefulness: A confirmatory factor analysis,” MIS Quarterly, vol. 17, No. 4, pp. 517-527, 1990.

315