Skip to main content
Log in

A model of online shopping cart abandonment: evidence from e-tail clickstream data

  • Original Empirical Research
  • Published:
Journal of the Academy of Marketing Science Aims and scope Submit manuscript

Abstract

This research investigates online consumer behavior in an e-commerce context with a focus on consumer online shopping cart use and subsequent cart abandonment. A model rooted in the Uses and Gratifications Theory, the Unified Theory of Acceptance and Use of Technology, and the concept of the purchase funnel is developed to explain the predicted relationships. Empirical findings based on clickstream data show that returning to an existing cart increases the subsequent cart use and decreases cart abandonment. Conversely, viewing clearance pages and viewing a large number of product reviews increases both cart use and cart abandonment. Browsing product pages decreases cart use, and increases cart abandonment. The moderating role of smartphone-based shopping is also examined, with the moderating effects primarily occurring early in the purchase funnel affecting cart use, and influencing cart abandonment to a smaller degree. Theoretical contributions and managerial implications for digital marketers are provided.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. CMP analysis was conducted as a robustness check. The CMP model fit was substantially worse (BIC = 20,333; log-likelihood = −9383) than the fit of the ZIP and CLGT models. While a majority of the coefficients was consistent, there were some changes. For cart use, a previously significant interaction between existing cart and device type became non-significant, while the main effect of clearance page became negative. For cart abandonment, visiting clearance page, cart removal, and number of products seen had a significant and negative effect. A possible reason for the changes is simultaneous, rather than sequential estimation. We argue that based on cart use being a condition that has to occur before cart can be abandoned, sequential modeling is more appropriate. Heteroskedasticity could also render CMP results to be inconsistent.

References

  • Aiken, L. S., & West, S. G. (1991). Multiple regression testing and interpreting interactions. Sage Publications.

    Google Scholar 

  • Anupindi, R., Dada, M., & Gupta, S. (1998). Estimation of consumer demand with stock-out based substitution: An application to vending machine products. Marketing Science, 17(4), 406–423.

    Article  Google Scholar 

  • Atkins, D. C., & Gallop, R. J. (2007). Rethinking how family researchers model infrequent outcomes: A tutorial on count regression and zero-inflated models. Journal of Family Psychology, 21(4), 726–735.

    Article  Google Scholar 

  • Azoulay, P., Graff Zivin, J. S., & Wang, J. (2010). Superstar extinction. The Quarterly Journal of Economics, 125(2), 549–589.

    Article  Google Scholar 

  • Batra, R., & Keller, K. L. (2016). Integrating marketing communications: New findings, new lessons, and new ideas. Journal of Marketing, 80(6), 122–145.

    Article  Google Scholar 

  • Bell, D. R., Corsten, D., & Knox, G. (2011). From point of purchase to path to purchase: How preshopping factors drive unplanned buying. Journal of Marketing, 75(1), 31–45.

    Article  Google Scholar 

  • Bhatnagar, A., & Papatla, P. (2019). Do habits influence the types of information that smartphone shoppers seek? Journal of Business Research, 94(1), 89–98.

    Article  Google Scholar 

  • Blumler, J. G. (1979). The role of theory in uses and gratifications studies. Communication Research, 6(1), 9–36.

    Article  Google Scholar 

  • Breugelmans, E., Campo, K., & Gijsbrechts, E. (2006). Opportunities for active stock-out management in online stores: The impact of the stock-out policy on online stock-out reactions. Journal of Retailing, 82(3), 215–228.

    Article  Google Scholar 

  • Bucklin, R. E., & Sismeiro, C. (2003). A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research, 40(3), 249–267.

    Article  Google Scholar 

  • Bucklin, R. E., & Sismeiro, C. (2009). Click here for internet insight: Advances in clickstream data analysis in marketing. Journal of Interactive Marketing, 23(1), 35–48.

    Article  Google Scholar 

  • Campo, K., Gijsbrechts, E., & Nisol, P. (2000). Towards understanding consumer response t stock-outs. Journal of Retailing, 76(2), 219–242.

    Article  Google Scholar 

  • Campo, K., Gijsbrechts, E., & Nisol, P. (2004). Dynamics in consumer response to product unavailability: Do stock-out reactions signal response to permanent assortment reductions? Journal of Business Research, 57(8), 834–843.

    Article  Google Scholar 

  • Castro, I. A., Morales, A. C., & Nowlis, S. M. (2013). The influence of disorganized shelf displays and limited product quantity on consumer purchase. Journal of Marketing, 77(4), 118–133.

    Article  Google Scholar 

  • Cialdini, R. B. (2001). Influence: Science and practice. Allyn & Bacon.

    Google Scholar 

  • Chatterjee, P., Hoffman, D. L., & Novak, T. P. (2003). Modeling the clickstream: Implications for web-based advertising efforts. Marketing Science, 22(4), 520–541.

    Article  Google Scholar 

  • Chen, Y., & Xie, J. (2008). Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 54(3), 477–491.

    Article  Google Scholar 

  • Cho, C.-H., Kang, J., & Cheon, H. (2006). Online shopping hesitation. Cyberpsychology & Behavior, 9(3), 261–274.

    Article  Google Scholar 

  • Close, A., & Kukar-Kinney, M. (2010). Beyond buying: Motivations behind consumers' online shopping cart use. Journal of Business Research, 63(9–10), 986–992.

    Article  Google Scholar 

  • Close, A., Kukar-Kinney, M., & Benusa, T. K. (2012). Toward a theory of consumer electronic shopping cart behavior: Motivations of e-cart use and abandonment. In A. G. Close (Ed.), Online consumer behavior: Theory and research in social media, advertising and e-tail (pp. 323–343). Routledge.

    Chapter  Google Scholar 

  • Danaher, P., Mullarkey, G., & Essegaier, S. (2006). Factors affecting web site visit duration: A cross-domain analysis. Journal of Marketing Research, 43(2), 182–194.

    Article  Google Scholar 

  • De Haan, E., Kannan, P. K., Verhoef, P. C., & Wiesel, T. (2018). Device switching in online purchasing: Examining the strategic contingencies. Journal of Marketing, 82(5), 1–19.

    Article  Google Scholar 

  • Eisend, M. (2008). Explaining the impact of scarcity appeals in advertising: The mediating role of perceptions of susceptibility. Journal of Advertising, 37(3), 33–40.

    Article  Google Scholar 

  • Evanschitzky, H., Gopalkrishnan, R., Iyer, J. H., & Dieter, A. (2004). E-satisfaction: A re-examination. Journal of Retailing, 80(3), 239–247.

    Article  Google Scholar 

  • Garnefeld, I., Eggert, A., Helm, S. V., & Tax, S. S. (2013). Growing existing customers’ revenue streams through customer referral programs. Journal of Marketing, 77(4), 17–32.

    Article  Google Scholar 

  • Germann, F., Ebbes, P., & Grewal, R. (2015). The chief marketing officer matters! Journal of Marketing, 79(3), 1–22.

    Article  Google Scholar 

  • Haider, S. W., Zhuang, G., Ikram, A., & Anwar, B. (2020). Consumers' device choice in e-retail: Do regulatory focus and chronotype matter? KSII Transactions on Internet and Information Systems, 14(1), 148–167.

    Google Scholar 

  • Huang, G.-H., Korfiatis, N., & Chang, C.-T. (2018). Mobile shopping cart abandonment: The roles of conflicts, ambivalence, and hesitation. Journal of Business Research, 85(1), 165–174.

    Article  Google Scholar 

  • Huang, P., Lurie, N., & Mitra, S. (2009). Searching for experience on the web: An empirical examination of consumer behavior for search and experience goods. Journal of Marketing, 73(2), 55–69.

    Article  Google Scholar 

  • Hubert, M., Blut, M., Brock, C., Backhaus, C., & Eberhardt, T. (2017). Acceptance of smartphone-based Mobile shopping: Mobile benefits, customer characteristics, perceived risks, and the impact of application context. Psychology & Marketing, 34(2), 175–194.

    Article  Google Scholar 

  • Inman, J. J., & Nikolova, H. (2017). Shopper-facing retail technology: A retailer adoption decision framework incorporating shopper attitudes and privacy concerns. Journal of Retailing, 93(1), 7–28.

    Article  Google Scholar 

  • Inman, J. J., Peter, A. C., & Raghubir, P. (1997). Framing the deal: The role of restrictions in accentuating deal value. Journal of Consumer Research, 24(1), 68–79.

    Article  Google Scholar 

  • Jang, S., Prasad, A., & Ratchford, B. T. (2012). How consumers use product reviews in the purchase decision process. Marketing Letters, 23(3), 825–838.

    Article  Google Scholar 

  • Jessup, R. K., Veinott, E. S., Todd, P. M., & Busemeyer, J. R. (2009). Leaving the store empty-handed: Testing explanations for the too-much-choice effect using decision field theory. Psychology & Marketing, 26(3), 299–320.

    Article  Google Scholar 

  • Johnson, G. A., Lewis, R. A., & Nubbemeyer, E. A. (2017). Ghost ads: Improving the economics of measuring online ad effectiveness. Journal of Marketing Research, 54(6), 867–884.

    Article  Google Scholar 

  • Kaatz, C., Brock, C., & Figura, L. (2019). Are you still online or are you already mobile?–predicting the path to successful conversions across different devices. Journal of Retailing and Consumer Services, 50(1), 10–21.

  • Ko, H., Cho, C. H., & Roberts, M. S. (2005). Internet uses and gratifications: A structural equation model of interactive advertising. Journal of Advertising, 34(2), 57–70.

    Article  Google Scholar 

  • Kukar-Kinney, M., & Close, A. (2010). The determinants of shopping cart abandonment. Journal of the Academy of Marketing Science, 38(2), 240–250.

    Article  Google Scholar 

  • Kukar-Kinney, M., Scheinbaum, A. C., & Schaefers, T. (2016). Compulsive buying in online daily deal settings: An investigation of motivations and contextual elements. Journal of Business Research, 69(2), 691–699.

    Article  Google Scholar 

  • Kukar-Kinney, M., & Xia, L. (2017). The effectiveness of number of deals purchased in influencing consumer response to daily deal promotions: A cue utilization approach. Journal of Business Research, 79(10), 189–197.

    Article  Google Scholar 

  • Lambert, D. (1992). Zero-inflated Poisson regression with an application to defects in manufacturing. Technometrics, 34(1), 1–14.

    Article  Google Scholar 

  • Li, H., & Kannan, P. K. (2014). Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. Journal of Marketing Research, 51(1), 40–56.

  • Li, J., Abbasi, A., Cheema, A., & Abraham, L. B. (2020). Path to purpose? How online customer journeys differ for hedonic versus utilitarian purchases. Journal of Marketing, 84(4), 127–146.

    Article  Google Scholar 

  • Li, J., Luo, X., Lu, X., & Moriguchi, T. (2021). The double-edged effects of e-commerce cart retargeting: Does retargeting too early backfire? Journal of Marketing, 85(4), 123–140.

    Article  Google Scholar 

  • Mallapragada, G., Chandukala, S. R., & Liu, Q. (2016). Exploring the effects of “what” (product) and “where” (website) characteristics on online shopping behavior. Journal of Marketing, 80(2), 21–38.

    Article  Google Scholar 

  • Maslowska, E., Malthouse, E. C., & Bernritter, S. F. (2017). Too good to be true: The role of online reviews’ features in probability to buy. International Journal of Advertising, 36(1), 142–163.

    Article  Google Scholar 

  • McQuail, D. (1987). Mass communication theory: An introduction (2nd ed.). Sage.

    Google Scholar 

  • Meola, A. (2020). Rise of m-commerce: Mobile-commerce shopping stats & trends in 2021. Retrieved April 8, 2021 from https://www.businessinsider.com/mobile-commerce-shopping-trends-stats

  • Miyazaki, A. D., Grewal, D., & Goodstein, R. C. (2005). The effect of multiple extrinsic cues on quality perceptions: A matter of consistency. Journal of Consumer Research, 32(1), 146–153.

    Article  Google Scholar 

  • Moore, S., & Mathews, S. (2008). An exploration of online shopping cart abandonment syndrome–a matter of risk and reputation. Journal of Website Promotion, 2(1–2), 71–88.

    Google Scholar 

  • Moe, W. W. (2003). Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology, 13(1–2), 29–39.

    Article  Google Scholar 

  • Moe, W. W. (2006). An empirical two-stage choice model with varying decision rules applied to internet clickstream data. Journal of Marketing Research, 43(4), 680–692.

    Article  Google Scholar 

  • Natarajan, T., Balasubramanian, S., & Kasilingam, D. (2018). The moderating role of device type and age of users on the intention to use mobile shopping applications. Technology in Society, 53, 79–90.

    Article  Google Scholar 

  • O’Donohoe, S. (1994). Advertising uses and gratifications. European Journal of Marketing, 28(8/9), 52–75.

    Article  Google Scholar 

  • Oliver, R. L., & Shor, M. (2003). Digital redemption of coupons: Satisfying and dissatisfying effects of promotion codes. Journal of Product and Brand Management, 12(2), 121–134.

    Article  Google Scholar 

  • Rajamma, R., Paswan, A., & Hossain, M. (2009). Why do shoppers abandon shopping cart? Perceived waiting time, risk, and transaction inconvenience. Journal of Product and Brand Management, 18(3), 188–197.

    Article  Google Scholar 

  • Roggeveen, A. L., Grewal, D., Townsend, C., & Krishnan, R. (2015). The impact of dynamic presentation format on consumer preferences for hedonic products and services. Journal of Marketing, 79(6), 34–49.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika,70(1), 41–55.

    Article  Google Scholar 

  • Rubin, D., Martins, C., Ilyuk, V., & Hildebrand, D. (2020). Online shopping cart abandonment: A consumer mindset perspective. Journal of Consumer Marketing, 37(5), 487–499.

    Article  Google Scholar 

  • Rutz, O. J., & Watson, G. F. (2019). Endogeneity and marketing strategy research: An overview. Journal of the Academy of Marketing Science, 47(3), 479–498.

    Article  Google Scholar 

  • Sahni, N., Narayanan, S., & Kalyanam, K. (2019). An experimental investigation of the effects of retargeted advertising: The role of frequency and timing. Journal of Marketing Research, 56(3), 401–418.

    Article  Google Scholar 

  • Scheibehenne, B., Greifeneder, R., & Todd, P. M. (2010). Can there ever be too many options? A meta-analytic review of choice overload. Journal of Consumer Research, 37(3), 409–425.

    Article  Google Scholar 

  • Shi, S. W., & Zhang, J. (2014). Usage experience with decision aids and evolution of online purchase behavior. Marketing Science, 33(6), 871–882.

    Article  Google Scholar 

  • Sismeiro, C., & Bucklin, R. E. (2004). Modeling purchase behavior at an e-commerce web site: A task-completion approach. Journal of Marketing Research, 41(3), 306–323.

    Article  Google Scholar 

  • Sloot, L. M., Verhoef, P. C., & Franses, P. H. (2005). The impact of brand equity and the hedonic level of products on consumer stock-out reactions. Journal of Retailing, 81(1), 15–34.

    Article  Google Scholar 

  • Smith, S. A., & Achabal, D. D. (1998). Clearance pricing and inventory policies for retail chains. Management Science, 44(3), 285–300.

    Article  Google Scholar 

  • Song, J.-D. (2019). A study on online shopping cart abandonment: A product category perspective. Journal of Internet Commerce, 18(4), 337–368.

    Article  Google Scholar 

  • Statista. (2020). Online shopping cart abandonment rate in selected industries. Retrieved March 2020 from https://www.statista.com/statistics/457078/category-cart-abandonment-rate-worldwide/

  • Steinhart, Y., Mazursky, D., & Kamins, M. (2013). The process by which product availability triggers purchase. Marketing Letters, 24(3), 217–228.

    Article  Google Scholar 

  • Szymanski, D. M., & Hise, R. T. (2000). E-satisfaction: An initial examination. Journal of Retailing, 76(3), 309–322.

    Article  Google Scholar 

  • Tang, H., & Lin, X. (2019). Curbing shopping cart abandonment in c2c markets—An uncertainty reduction approach. Electronic Markets, 29(3), 533–552.

    Article  Google Scholar 

  • Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

  • Wagner, G., Schramm-Klein, H., & Steinmann, S. (2020). Online retailing across e-channels and e-channel touchpoints: Empirical studies of consumer behavior in the multichannel e-commerce environment. Journal of Business Research, 107, 256–270.

    Article  Google Scholar 

  • Wang, R., Malthouse, E. C., & Krishnamurthi, L. (2015). On the go: How mobile shopping affects customer purchase behavior. Journal of Retailing, 91(2), 217–234.

    Article  Google Scholar 

  • Wolin, L. D., & Korgaonkar, P. (2003). Web advertising: Gender differences in beliefs, attitudes and behavior. Internet Research, 13(5), 375–385.

    Article  Google Scholar 

  • Xu, Y., & Huang, J.-S. (2015). Factors influencing cart abandonment in the online shopping process. Social Behavior and Personality: An International Journal, 43(10), 1617–1627.

  • Yli-Renko, H., & Janakiraman, R. (2008). How customer portfolio affects new product development in technology-based entrepreneurial firms. Journal of Marketing, 72(5), 131–148.

    Article  Google Scholar 

  • Zhang, L., & Zhang, W. (2013). Real-time internet news browsing: Information vs. experience-related gratifications and behaviors. Computers in Human Behavior, 29(6), 2712–2721.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Scott Baier, Jason Duan and Ryan Mullins for their advice and guidance regarding this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Monika Kukar-Kinney.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

All authors importantly contributed to this research.

Marnik Dekimpe served as Area Editor for this article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kukar-Kinney, M., Scheinbaum, A.C., Orimoloye, L.O. et al. A model of online shopping cart abandonment: evidence from e-tail clickstream data. J. of the Acad. Mark. Sci. 50, 961–980 (2022). https://doi.org/10.1007/s11747-022-00857-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11747-022-00857-8

Keywords