Printer Friendly

Factors influencing cart abandonment in the online shopping process.

A virtual shopping cart is a necessary tool for online shopping. A common problem encountered by e-commerce businesses is that between 25% and 88% of consumers abandon the products in their shopping cart, resulting in the last-minute loss of a transaction opportunity (Kukar-Kinney & Close, 2010); therefore, understanding how to reduce cart abandonment is a priority for e-commerce businesses. Close and Kukar-Kinney (2010) examined the factors that impact on electronic cart use, and found that taking advantage of price promotions, organization (i.e., using an online cart in place of a wish list), entertainment, and current purchase intention had a significant influence on cart use. The authors built a structural model of cart abandonment, and concluded that entertainment value, use of the cart as a research and organizational tool to sort items of interest as part of a purposeful search, and waiting for a sale directly influenced cart abandonment, and concern about costs indirectly influenced cart abandonment (Kukar-Kinney & Close, 2010). According to Rajamma, Paswan, and Hossain (2009), waiting time, risk, and transaction inconvenience significantly influence cart abandonment. As an extension to these previous studies, in this study we addressed cart abandonment from a new perspective, whereby the factors that influence cart abandonment are proposed to exist at every stage of the online shopping process. To illustrate the process, we constructed a model of cart abandonment, with the aim of determining the direct and indirect impact of each shopping stage on cart abandonment.

Conceptual Framework and Hypotheses

When shopping online, consumers log onto a website, browse through the web pages to choose goods, place items of interest into the virtual cart, compare and research items within the cart, and finally enter into payment, or abandon the cart (see shaded parts in Figure 1). Throughout this online shopping process, consumers are either directly or indirectly influenced by a number of factors (see Figure 1), including perceived transaction inconvenience, perceived cost, perceived risk, and comparisons with goods available on external websites (Rajamma et al., 2009). To focus on cart abandonment behavior, in this paper, we tested the structural frame model depicted as a solid line in Figure 1.

Consumers are likely to abort the online shopping process before performing research on products within the cart if they feel that the loading speed of web pages is too slow, the transaction process is too complicated, or the quality of the goods is questionable (Harrison-Walker, 2002). Therefore, in contrast to Rajamma et al. (2009), who held that transaction inconvenience influences cart abandonment, we believe that perceived transaction inconvenience, such as slow loading speed of web pages and a complex transaction process, will influence whether or not consumers enter the cart use stage. As a result, perceived transaction inconvenience will not have a significant impact on organization and research of products within the cart. Thus, we formed the following hypothesis:

Hypothesis 1: There will not be a significant link between perceived transaction inconvenience and organization and research of products within the cart.

Transaction cost has been found to have a significant effect on the online shopping process (Wu, Chen, Chen, & Cheng, 2014), including cart use behavior (Magill, 2005). Moreover, online, compared to offline, consumers are more price sensitive and expect lower overall costs (Xia & Monroe, 2004); thus, the online consumer may carry out more research and comparisons to find a suitable price. If the price of the goods is higher than expected, online consumers are inclined to look for goods with a better price/performance ratio. At this time, their research efforts will increase, and they will perform more organization and research of products within the cart (Kukar-Kinney & Close, 2010). The additional comparison of goods will help consumers confirm the price/performance ratio, thus allowing them to make the optimal choice. Hence, we formed the following hypothesis:

Hypothesis 2: High perceived product costs will exert a significantly negative influence on organization and research of items within the cart.

Risk is an individual's perception of the uncertainty and adverse consequences of engaging in an activity (Chang & Tseng, 2013; Dowling & Staelin, 1994), and perceived risk often relates to research behavior. Dowling and Staelin (1994) found that increased perceived risk resulted in an increase in risk-reduction activities, such as the use of information-search activities. Cho, Kang, and Cheon (2006) further found that perceived uncertainty has an impact on shopping cart abandonment; therefore, if consumers feel that the risk of buying a certain product is high, they will become more cautious about making the purchase. As a result, more organization and research of products within the cart will follow (Kukar-Kinney & Close, 2010; Rajamma et al., 2009). Hence, we formed the following hypothesis:

Hypothesis 3: High purchase risk will have a significantly positive influence on organization and research of products within the cart and, thus, on cart abandonment behavior.

When a consumer performs more organization and research with regard to items within the cart, they will think more about the quality and value of the product, and, therefore, the likelihood of an impulsive purchase will decrease (Strack, Werth, & Deutsch, 2006). Therefore, the more consumers study a product, the more cautious they will become about making the purchase, and the more likely they will be to abandon the purchase (Kukar-Kinney & Close, 2010). On the other hand, the more organization, research, and comparison behavior the consumers undertake before a purchase, the higher is the probability that they will not follow through with purchasing the items in the online cart (Paden & Stell, 2010). Therefore, we formed the following hypothesis:

Hypothesis 4: A high degree of organization and research of products within the cart will have a significantly positive impact on cart abandonment.

When consumers add products to a cart, both the possibility of buying and payment intention increase. On the other hand, payment intention might be formed after organization and research of products within the cart. Moreover, the consumer will not enter into payment before checking the goods within the cart. Therefore, product organization and research is an antecedent in the formation of payment intention (Close & Kukar-Kinney, 2010). We, therefore, formed the following hypothesis:

Hypothesis 5: A high degree of organization and research of products within the cart will have a significantly positive impact on payment intention.

When shopping online, consumers are easily influenced by other e-commerce websites, often conducting comparisons of the quality and price of, and method of payment for, a product, which affects cart abandonment behavior. For example, Chevalier and Goolsbee (2003) found that online, versus offline, consumers had higher price sensitivity because of online comparison. If the product on the current website fails to compete with that on other websites in terms of quality and price, there is a high possibility that consumers will abandon the purchase (Kamakura & Moon, 2009). Thus, we formed the following hypothesis:

Hypothesis 6: A poor result from comparison with external websites will have a significantly positive influence on cart abandonment.

Behavior is guided by intentions (Ouellette & Wood, 1998); thus, when people form an intention, the possibility of conducting the corresponding behavior substantially increases. Therefore, once the payment intention is formed, consumers will enter payment directly, which is the final step to complete the purchase. If there are no disturbances at this point of the transaction, the consumer will buy the product and the possibility of cart abandonment will be decreased. That is to say, the stronger the payment intention, the lower is the possibility of cart abandonment. Thus, our final hypothesis was as follows:

Hypothesis 7: A strong payment intention will have a significantly negative influence on cart abandonment.

Method

Participants and Procedure

To test our hypotheses, we conducted a survey with users of the online shopping website of a communication company in China, which specializes in selling cell phones and prepaid cell phone cards. We collected 300 online comments that consumers left after making a purchase, and analyzed these comments to identify the main problems that consumers reported meeting when shopping on this website. These problems were used to construct the measurement scale. Participants received a 10 RMB (approximately US$1.50) discount coupon in exchange for taking part in the study. Of 210 people who responded to the online survey, 194 completed it and comprised the final study sample. Around half (101, 52.1%) of the participants were male and 93, (47.9%) were female, with 116 (59.8%) aged between 18 and 30 years, 56 (28.9%) between 31 and 40 years, 16 (8.2%) between 41 and 50 years, and six (3.1%) over 51 years. Participants held 15 different kinds of occupation, including (32.5%) employed as company staff, 40 (20.6%) research and development personnel, 29 (14.9%) full-time students, 21 (10.8%) technical staff, and 41 (21.1%) another type of occupation.

Instruments

The items used were sourced from the published literature (Close & Kukar-Kinney, 2010; Kukar-Kinney & Close, 2010; Rajamma et al., 2009) and the abovementioned rearrangement of online comments. These items were divided across the following seven factors: perceived transaction inconvenience, perceived cost, perceived risk, organization and research of products within the cart, payment intention, comparisons with external websites, and cart abandonment. Each factor was measured with three items and unless otherwise noted, was rated on a 5-point Likert scale anchored by 1 = strongly disagree and 5 = strongly agree.

Perceived transaction inconvenience. The items used to evaluate perceived transaction inconvenience were based on online comments and a measure used by Rajamma et al. (2009). Items covered the complexity of the transaction registry, the website loading time, and the complexity of the transaction process.

Perceived cost. The items used to evaluate perceived cost were based on online comments and a measure used by Kukar-Kinney and Close (2010). Items covered perception of size of discounts, shipping fee, and sales promotion.

Perceived risk. The items used to evaluate perceived risk were based on online comments and measures used by Rajamma et al. (2009) and Kukar-Kinney and Close (2010). Items covered the perceived personal information risk, perceived payment security risk, and perceived privacy.

Organize and research products within the cart. The items used to evaluate organization and research of products within the cart were based on measures used by Close and Kukar-Kinney (2010) and Kukar-Kinney and Close (2010). Items covered the use of shopping carts for making comments on goods, for collection of information, and as a tool for research purchases.

Payment intention. The items used to evaluate payment intention were based on the definition of the term, namely the intention to pay for goods placed in the online cart. Items covered the willingness to pay for the goods, the likelihood of exiting the website before making payment, and the strength of the desire to buy the goods.

Comparisons with external websites. The items used to evaluate comparisons with external websites were based on the definition of the term and online comments. Items covered the comparisons of quality and price, and online comments on external websites.

Cart abandonment. The items used to evaluate cart abandonment were related to self-reported abandonment of online shopping carts in the past month. The response options were "I never abandon a shopping cart," "I usually do not abandon the shopping cart," "Whether or not I abandon a shopping cart depends on the situation," "I occasionally abandon a shopping cart," and "I always abandon a shopping cart."

Data Analysis

Data analysis was conducted via structural equation modeling (SEM). This method is used to study relationships among variables, and is suitable for analyzing online behavior, such as the process of cart abandonment. Hypothetical models were analyzed via LISREL 8.7, and each model produced a reasonable fit according to statistical indices of comparative fit index (CFI), normed fit index (NFI), and standardized root mean square residual (SRMR).

Results

We first conducted an exploratory factor analysis to evaluate the validity and reliability of the measures. The factor loading of all items was higher than .6. For all factors, the composite reliability and Cronbach's alpha were greater than .7, and the average variance extracted was greater than .5 (see Table 1). These results indicate that the internal consistency and external validity were acceptable (Fornell & Larcker, 1981).

SEM with maximum likelihood estimation in LISREL 8.7 was used to test the conceptual model. The goodness-of-fit statistics of the model met the recommended criteria (Hu & Bentler, 1998): CFI = .95, NFI = .90, SRMR = .07, [chi square] = 251.50, and df = 138. These results indicate that the model fit the data well.

All hypotheses were supported by the results. Perceived transaction inconvenience had no significant effect on organization and research of products within the cart ([beta] = .07, SE = 0.10, t = 0.67), supporting H1. The negative link between high perceived cost and organization and research of products within the cart was significant ([beta] = .24, SE = 0.11, t = 2.25), supporting H2. High perceived risk had a significantly negative impact on organization and research of products within the cart ([beta] = .27, SE = 0.11, t = 2.48), supporting H3. Organization and research of products within the cart had a significantly positive impact on cart abandonment ([beta] = .16, SE = 0.07, t = 2.36), supporting H4. The positive link between organization and research of products within the cart and payment intention was significant ([beta] = .61, SE = 0.12, t = 5.03), supporting H5. Comparisons with external websites had a significantly positive influence on cart abandonment ([beta] = .14, SE = 0.05, t = 2.69), supporting H6. Payment intention had a significantly negative influence on cart abandonment ([beta] = -.16, SE = 0.06, t = -2.61), supporting H7. In addition, perceived cost ([beta] = -.06, SE = 0.05, t = -1.15) and perceived risk ([beta] = .00, SE = 0.05, t = 0.07) did not have a significant direct effect on cart abandonment, which is consistent with our conceptual model of online cart abandonment.

Discussion

Results and Research Implications

In this paper, we constructed a process model comprising direct and indirect factors that influence cart abandonment, which enabled us to examine why people systematically abandon virtual shopping carts. On the basis of this process model, we constructed a structural equation model and verified the cart abandonment process through empirical analysis.

According to the proposed model, organization and research of products within the cart was a key variable influencing cart abandonment. Perceived cost and perceived risk did not directly affect cart abandonment but did exert an indirect influence on cart abandonment through the mediator of organization and research of products within the cart. That is to say, the higher the perceived cost or perceived risk, the more likely consumers were to organize and research the products within their cart. The indirect effect of perceived cost on cart abandonment that we found is consistent with the results of Kukar-Kinney and Close (2010), but our finding with regard to the effect of perceived risk is not. On the basis of the conclusions of previous researchers (Dowling & Staelin, 1994), we believe that a higher level of perceived risk will result in more organization and research of products within the cart, rather than cart abandonment.

Our results also showed that perceived transaction inconvenience had no significant effect on organization and research of products within the cart. This may be because when consumers found a transaction inconvenient, they abandoned the purchase early in the process rather than reaching the stage of putting the goods into the shopping cart. This result contradicts the conclusion of Rajamma et al. (2009), and we believe that this is because Rajamma et al. did not take the intermediary role of organization and research of products within the cart into consideration.

Organization and research of products within the cart had a positive impact on payment intention, meaning that the more the consumer organized and researched the products within their cart, the stronger their payment intention became. Further, organization and research of items within the cart exerted a positive effect on cart abandonment, meaning that the more research the consumer performed, the more cautious he or she became during the purchasing process (Chevalier & Goolsbee, 2003), and, thus, the likelihood of abandoning the purchase increased. These results are in line with those of Kukar-Kinney and Close (2010).

Comparisons with external websites had a significant effect on cart abandonment, meaning that if the goods were of lower quality, higher price, or had received more negative comments than those on external websites, the possibility of cart abandonment increased. This has not been discussed previously in the literature, but is consistent with Chevalier and Goolsbee's (2003) findings that consumers became more cautious as a result of online comparison.

The significant negative influence of payment intention on cart abandonment indicates that when payment intention increased, the possibility of payment also increased, and the possibility of cart abandonment decreased. This also has not previously been discussed, but is in line with the conclusion that behavior is guided by intentions (Ouellette & Wood, 1998).

Study Limitations and Directions for Future Research

This study has weaknesses that should be noted. First, the effect of individual behavior variables as regards purchasing frequency (Fenech, 2002) and entertainment motivations (Kukar-Kinney & Close, 2010) was not tested, and could be studied in the future. Second, our conclusions are based on the results of a self-report survey. Although this method has previously been used to study cart abandonment (e.g., Close & Kukar-Kinney, 2010; Kukar-Kinney & Close, 2010), it is necessary to conduct studies using real purchase data. Third, our findings are based on the website of a company in the communication industry, and so must be further tested before they can be extrapolated to other online sales industries. Fourth, the survey was completed through the website, so the participants were not a random sample, and it is necessary to conduct further research using other sampling methods.

http://dx.doi.org/10.2224/sbp.2015.43.10.1617

YIN XU

Central University of Finance and Economics

JIN-SONG HUANG

Beihang University

Yin Xu, Department of Marketing, Central University of Finance and Economics; Jin-Song Huang, Department of Marketing, Beihang University.

This research was supported by the National Natural Science Foundation of China (71172015 and 71372006).

Correspondence concerning this article should be addressed to: Jin-Song Huang, Department of Marketing, Beihang University, Beijing 100191, People's Republic of China. Email: huangjs@ buaa.edu.cn

References

Chang, E.-C., & Tseng, Y.-F. (2013). Research note: E-store image, perceived value and perceived risk. Journal of Business Research, 66, 864-870. http://doi.org/cgbm4r

Chevalier, J., & Goolsbee, A. (2003). Measuring prices and price competition online: Amazon.com and BarnesandNoble.com. Quantitative Marketing and Economics, 1, 203-222. http://doi.org/ c2kks7

Cho, C.-H., Kang, J., & Cheon, H. J. (2006). Online shopping hesitation. CyberPsychology & Behavior, 9, 261-274. http://doi.org/frvb8j

Close, A. G., & Kukar-Kinney, M. (2010). Beyond buying: Motivations behind consumers' online shopping cart use. Journal of Business Research, 63, 986-992. http://doi.org/bj7ptz

Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21, 119-134. http://doi.org/dccf2c

Fenech, T. (2002, December). Antecedents to web cart abandonment. Paper presented at the 16th Annual Australia New Zealand Academy of Management Conference, Beechworth, Australia.

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement errors: Algebra and statistics. Journal of Marketing Research, 18, 382-388. http:// doi.org/cwp

Harrison-Walker, J. L. (2002). If you build it, will they come? Barriers to international e-marketing. Journal of Marketing Theory and Practice, 10, 12-21.

Hu, L.-T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424^53. http://doi.org/c49

Kamakura, W., & Moon, S. (2009). Quality-adjusted price comparison of non-homogeneous products across Internet retailers. International Journal of Research in Marketing, 26, 189-196. http:// doi.org/bxh6q9

Kukar-Kinney, M., & Close, A. G. (2010). The determinants of consumers' online shopping cart abandonment. Journal of the Academy of Marketing Science, 38, 240-250. http://doi.org/fsxz8d

Magill, K. (2005). Building a better shopping cart. Multichannel Merchant, 1, 18-19.

Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124, 54-74. http:// doi.org/fsnx7d

Paden, N., & Stell, R. (2010, February). Virtual cart abandonment: Addressing hedonic and utilitarian shopping motives. Paper presented at the 17th Annual Conference of the American Society of Business and Behavioral Sciences, Las Vegas, NE, USA.

Rajamma, R., Paswan, A., & Hossain, M. (2009). Why do shoppers abandon shopping carts? Perceived waiting time, risk, and transaction inconvenience. Journal of Product & Brand Management, 18, 188-197. http://doi.org/fn996d

Strack, F., Werth, L., & Deutsch, R. (2006). Reflective and impulsive determinants of consumer behavior. Journal of Consumer Psychology, 16, 205-216. http://doi.org/dm6crj

Wu, L.-Y., Chen, K.-Y., Chen, P.-Y., & Cheng, S.-L. (2014). Perceived value, transaction cost, and repurchase-intention in online shopping: A relational exchange perspective. Journal of Business Research, 67, 2768-2776. http://doi.org/55f

Xia, L., & Monroe, K. B. (2004). Price partitioning on the Internet. Journal of Interactive Marketing, 18, 63-73. http://doi.org/crzkx8

Table 1. Summary of Measurement Scales

Measure                           M     Loadings   AVE   CR    [alpha]

Perceived transaction inconvenience

The process of transaction       3.37     .76      .74   .79     .79
registry is rather complex.

The speed of web pages           3.27     .76
loading is too slow.

The transaction process is       3.31     .85
too complicated.

Perceived cost

The discount for goods is too    3.48     .65      .66   .75     .75
small.

Shipping fees for the goods      3.41     .88
are too high.

There is no sales promotion      3.55     .77
for the product I want to buy.

Perceived risk

I'm afraid that my personal      3.65     .78      .73   .79     .77
information might be revealed.

I'm worried about the security   3.55     .62
of the payment process.

I'm worried about the security   3.50     .73
of my privacy.

Organize and research products within the cart

I use the shopping cart to       3.41     .75      .64   .74     .73
comment on the goods.

I use the shopping cart to       3.97     .71
collect and research product
information.

I use the shopping cart as a     3.80     .72
tool for product research.

Payment intention

I will pay for the goods I       3.17     .83      .67   .75     .76
have placed into the shopping
cart.

I will not exit the website      3.16     .86
before making payment for the
goods in the shopping cart.

I wish to buy the goods in the   3.56     .65
shopping cart.

Compare with external websites

Compared with external           3.95     .76      .72   .79     .78
websites, the products on this
website are of lower quality.

Compared with external           4.02     .77
websites, prices for the
products on this website are
higher.

I will abort the purchase if     3.81     .76
comments on external websites
about the product are bad.

Note. CR = composite reliability, AVE = average variance
extracted, [alpha] = Cronbach's alpha internal consistency
reliability.
COPYRIGHT 2015 Scientific Journal Publishers, Ltd.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2015 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Xu, Yin; Huang, Jin-Song
Publication:Social Behavior and Personality: An International Journal
Article Type:Report
Geographic Code:9CHIN
Date:Nov 1, 2015
Words:3801
Previous Article:Personality and worry: the role of intolerance of uncertainty.
Next Article:Concurrent use of an in-vehicle navigation system and a smartphone navigation application.
Topics:

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters