Skip to main content
Log in

The impact of multi-type online advertising on the consumer engagement transition

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

The explosion in the volume and variety of online advertising has contributed to constantly changing consumer engagement states by influencing their experience through the journey to purchase. In this paper, we use a latent Markov model to analyze the panel data from a large e-commerce firm, so as to explore changes in the engagement behavior and the latent engagement state transition of consumers under different advertising. Contrary to common belief, advertising does not always play an active role in communicating with consumers. Our findings show that the response of consumers to advertising is heterogeneous at different engagement levels, and this heterogeneity will be affected by the type of advertising and the number of different advertisements consumers contact. We explore the underlying reasons for the two-sided impact of advertising on consumer engagement, and thus expected to provide managerial insights for marketers who pay attention to consumer experience.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The data is not publicly available and can be obtained from the corresponding author by reasonable request.

Notes

  1. https://nielseniq.com/global/en/insights/analysis/2022/the-evolution-of-e-commerce-globally/.

  2. https://www.emarketer.com/content/worldwide-digital-ad-spending-year-end-update.

References

  1. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96.

    Article  Google Scholar 

  2. Voorveld, H. A., Van Noort, G., Muntinga, D. G., & Bronner, F. (2018). Engagement with social media and social media advertising: The differentiating role of platform type. Journal of Advertising, 47(1), 38–54.

    Article  Google Scholar 

  3. Gavilanes, J. M., Flatten, T. C., & Brettel, M. (2018). Content strategies for digital consumer engagement in social networks: Why advertising is an antecedent of engagement. Journal of Advertising, 47(1), 4–23.

    Article  Google Scholar 

  4. Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252–271.

    Article  Google Scholar 

  5. Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3), 253–266.

    Article  Google Scholar 

  6. Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of Interactive Marketing, 28(2), 149–165.

    Article  Google Scholar 

  7. Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring customer relationships beyond purchase. Journal of Marketing Theory and Practice, 20(2), 122–146.

    Article  Google Scholar 

  8. Calder, B. J., Isaac, M. S., & Malthouse, E. C. (2016). How to capture consumer experiences: A context-specific approach to measuring engagement: Predicting consumer behavior across qualitatively different experiences. Journal of Advertising Research, 56(1), 39–52.

    Article  Google Scholar 

  9. Todri, V., Ghose, A., & Singh, P. V. (2020). Trade-offs in online advertising: Advertising effectiveness and annoyance dynamics across the purchase funnel. Information Systems Research, 31(1), 102–125.

    Article  Google Scholar 

  10. Liu-Thompkins, Y. (2019). A decade of online advertising research: What we learned and what we need to know. Journal of Advertising, 48(1), 1–13.

    Article  Google Scholar 

  11. Shen, Q., & Miguel Villas-Boas, J. (2018). Behavior-based advertising. Management Science, 64(5), 2047–2064.

    Article  Google Scholar 

  12. Li, H., & Ma, L. (2020). Charting the path to purchase using topic models. Journal of Marketing Research, 57(6), 1019–1036.

    Article  Google Scholar 

  13. Wiesel, T., Pauwels, K., & Arts, J. (2011). Practice prize paper-marketing’s profit impact: Quantifying online and off-line funnel progression. Marketing Science, 30(4), 604–611.

    Article  Google Scholar 

  14. Verhoef, P. C., Venkatesan, R., McAlister, L., Malthouse, E. C., Krafft, M., & Ganesan, S. (2010). CRM in data-rich multichannel retailing environments: A review and future research directions. Journal of Interactive Marketing, 24(2), 121–137.

    Article  Google Scholar 

  15. Singh, V., Nanavati, B., Kar, A. K., & Gupta, A. (2022). How to maximize clicks for display advertisement in digital marketing? A reinforcement learning approach. Information Systems Frontiers, 51(1), 1–18.

    Google Scholar 

  16. Lee, Y.-J., Keeling, K. B., & Urbaczewski, A. (2019). The economic value of online user reviews with ad spending on movie box-office sales. Information Systems Frontiers, 21(4), 829–844.

    Article  Google Scholar 

  17. Hoban, P. R., & Bucklin, R. E. (2015). Effects of internet display advertising in the purchase funnel: Model-based insights from a randomized field experiment. Journal of Marketing Research, 52(3), 375–393.

    Article  Google Scholar 

  18. Yang, Y., & Zhai, P. (2022). Click-through rate prediction in online advertising: A literature review. Information Processing & Management, 59(2), 102853.

    Article  Google Scholar 

  19. Ruz-Mendoza, M. Á., Trifu, A., Cambra-Fierro, J., & Melero-Polo, I. (2021). Standardized vs. customized firm-initiated interactions: Their effect on customer gratitude and performance in a B2B context. Journal of Business Research, 133, 341–353.

    Article  Google Scholar 

  20. Alalwan, A. A. (2018). Investigating the impact of social media advertising features on customer purchase intention. International Journal of Information Management, 42, 65–77.

    Article  Google Scholar 

  21. Li, H. (2022). Converting free users to paid subscribers in the SaaS context: The impact of marketing touchpoints, message content, and usage. Production and Operations Management, 31(5), 2185–2203.

    Article  Google Scholar 

  22. Sreejesh, S., Paul, J., Strong, C., & Pius, J. (2020). Consumer response towards social media advertising: Effect of media interactivity, its conditions and the underlying mechanism. International Journal of Information Management, 54, 102155.

    Article  Google Scholar 

  23. Lu, C.-C., Wu, L., & Hsiao, W.-H. (2019). Developing customer product loyalty through mobile advertising: Affective and cognitive perspectives. International Journal of Information Management, 47, 101–111.

    Article  Google Scholar 

  24. Plume, C. J., & Slade, E. L. (2018). Sharing of sponsored advertisements on social media: A uses and gratifications perspective. Information Systems Frontiers, 20(3), 471–483.

    Article  Google Scholar 

  25. Leong, L.-Y., Hew, T.-S., Ooi, K.-B., & Dwivedi, Y. K. (2020). Predicting trust in online advertising with an SEM-artificial neural network approach. Expert Systems with Applications, 162, 113849.

    Article  Google Scholar 

  26. Martins, J., Costa, C., Oliveira, T., Gonçalves, R., & Branco, F. (2019). How smartphone advertising influences consumers’ purchase intention. Journal of Business Research, 94, 378–387.

    Article  Google Scholar 

  27. Yang, Y., Feng, B., & Zeng, D. (2021). Learning parameters for a generalized Vidale-Wolfe response model with flexible ad elasticity and word-of-mouth. IEEE Intelligent Systems, 36(5), 69–79.

    Article  Google Scholar 

  28. Tang, J., Zhang, P., & Wu, P. F. (2015). Categorizing consumer behavioral responses and artifact design features: The case of online advertising. Information Systems Frontiers, 17(3), 513–532.

    Article  Google Scholar 

  29. Mishra, S., & Malhotra, G. (2021). The gamification of in-game advertising: Examining the role of psychological ownership and advertisement intrusiveness. International Journal of Information Management, 61, 102245.

    Article  Google Scholar 

  30. Chen, Q., Feng, Y., Liu, L., & Tian, X. (2019). Understanding consumers’ reactance of online personalized advertising: A new scheme of rational choice from a perspective of negative effects. International Journal of Information Management, 44, 53–64.

    Article  Google Scholar 

  31. Venkatraman, V., Dimoka, A., Pavlou, P. A., Vo, K., Hampton, W., Bollinger, B., & Winer, R. S. (2015). Predicting advertising success beyond traditional measures: New insights from neurophysiological methods and market response modeling. Journal of Marketing Research, 52(4), 436–452.

    Article  Google Scholar 

  32. Ansari, A., Mela, C. F., & Neslin, S. A. (2008). Customer channel migration. Journal of Marketing Research, 45(1), 60–76.

    Article  Google Scholar 

  33. Braun, M., & Moe, W. W. (2013). Online display advertising: Modeling the effects of multiple creatives and individual impression histories. Marketing Science, 32(5), 753–767.

    Article  Google Scholar 

  34. Hu, Y., Du, R. Y., & Damangir, S. (2014). Decomposing the impact of advertising: Augmenting sales with online search data. Journal of Marketing Research, 51(3), 300–319.

    Article  Google Scholar 

  35. Danaher, P. J., Danaher, T. S., Smith, M. S., & Loaiza-Maya, R. (2020). Advertising effectiveness for multiple retailer-brands in a multimedia and multichannel environment. Journal of Marketing Research, 57(3), 445–467.

    Article  Google Scholar 

  36. Zantedeschi, D., Feit, E. M., & Bradlow, E. T. (2017). Measuring multichannel advertising response. Management Science, 63(8), 2706–2728.

    Article  Google Scholar 

  37. Kang, K., Lu, J., Guo, L., & Li, W. (2021). The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms. International Journal of Information Management, 56, 102251.

    Article  Google Scholar 

  38. Verhagen, T., Swen, E., Feldberg, F., & Merikivi, J. (2015). Benefitting from virtual customer environments: An empirical study of customer engagement. Computers in Human Behavior, 48, 340–357.

    Article  Google Scholar 

  39. Lee, D., Hosanagar, K., & Nair, H. S. (2018). Advertising content and consumer engagement on social media: Evidence from Facebook. Management Science, 64(11), 5105–5131.

    Article  Google Scholar 

  40. Phua, J., Lin, J.-S.E., & Lim, D. J. (2018). Understanding consumer engagement with celebrity-endorsed E-Cigarette advertising on instagram. Computers in Human Behavior, 84, 93–102.

    Article  Google Scholar 

  41. Bruce, N. I., Murthi, B., & Rao, R. C. (2017). A dynamic model for digital advertising: The effects of creative format, message content, and targeting on engagement. Journal of Marketing Research, 54(2), 202–218.

    Article  Google Scholar 

  42. Geng, S., Yang, P., Gao, Y., Tan, Y., & Yang, C. (2021). The effects of ad social and personal relevance on consumer ad engagement on social media: The moderating role of platform trust. Computers in Human Behavior, 122, 106834.

    Article  Google Scholar 

  43. Zucchini, W., & MacDonald, I. L. (2009). Hidden Markov models for time series: An introduction using R. Chapman and Hall/CRC.

    Book  Google Scholar 

  44. Bartolucci, F., Farcomeni, A., & Pennoni, F. (2012). Latent Markov models for longitudinal data. CRC Press.

    Book  Google Scholar 

  45. Bartolucci, F., Pandolfi, S., & Pennoni, F. (2017). LMest: An R package for latent Markov models for longitudinal categorical data. Journal of Statistical Software, 81, 1–38.

    Article  Google Scholar 

  46. Wu, S., Tan, Y., Chen, Y., & Liang, Y. (2022). How is mobile user behavior different? A hidden Markov model of cross-mobile application usage dynamics. Information Systems Research, 33(3), 1002–1022.

    Article  Google Scholar 

  47. Ascarza, E., Netzer, O., & Hardie, B. G. (2018). Some customers would rather leave without saying goodbye. Marketing Science, 37(1), 54–77.

    Article  Google Scholar 

  48. Chen, W., Wei, X., & Zhu, K. (2017). Engaging voluntary contributions in online communities: A hidden Markov model. Mis Quarterly, 42(1), 83–100.

    Article  Google Scholar 

  49. MacLahlan, G., & Peel, D. (2000). Finite mixture models. Wiley.

    Book  Google Scholar 

  50. Du, R. Y., & Kamakura, W. A. (2006). Household life cycles and lifestyles in the United States. Journal of Marketing Research, 43(1), 121–132.

    Article  Google Scholar 

  51. Bueno, M. L., Hommersom, A., Lucas, P. J., & Linard, A. (2017). Asymmetric hidden Markov models. International Journal of Approximate Reasoning, 88, 169–191.

    Article  Google Scholar 

  52. Bacci, S., Pandolfi, S., & Pennoni, F. (2014). A comparison of some criteria for states selection in the latent Markov model for longitudinal data. Advances in Data Analysis and Classification, 8, 125–145.

    Article  Google Scholar 

  53. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

    Article  Google Scholar 

  54. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.

    Article  Google Scholar 

  55. Dias, J. G., & Vermunt, J. K. (2007). Latent class modeling of website users’ search patterns: Implications for online market segmentation. Journal of Retailing and Consumer Services, 14(6), 359–368.

    Article  Google Scholar 

  56. Zhou, T., Yan, L., Wang, Y., & Tan, Y. (2022). Turn your online weight management from zero to hero: A multidimensional, continuous-time evaluation. Management Science, 68(5), 3507–3527.

    Article  Google Scholar 

  57. Bunch, D. S. (1988). A comparison of algorithms for maximum likelihood estimation of choice models. Journal of Econometrics, 38(1–2), 145–167.

    Article  Google Scholar 

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

    Article  Google Scholar 

  59. De Haan, E., Wiesel, T., & Pauwels, K. (2016). The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework. International Journal of Research in Marketing, 33(3), 491–507.

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by National Natural Science Foundation of China [Grant No. 71771122] and Postgraduate Research & Practice Innovation Program of Jiangsu Province [Grant Nos. KYCX21_0393, KYCX22_0386].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Li Li.

Ethics declarations

Conflict of interest

The authors declare that there are no conflicts of interest

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, B., Li, L., Wang, Q. et al. The impact of multi-type online advertising on the consumer engagement transition. Electron Commer Res (2023). https://doi.org/10.1007/s10660-023-09775-5

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10660-023-09775-5

Keywords

Navigation