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Gender difference in visual attention to digital content of place-based advertising: a data-driven scientific approach

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Abstract

Due to the greater integration of digital technology within advertising and e-commerce, academics and practitioners need a better understanding of advertising effects in ecologically valid environments. This in-market study focuses on gender differences to investigate different types of visual attention for place-based advertising in a digital marketing context. This study adopts a data-driven scientific approach and demonstrates that gender differences can assess shoppers’ viewing behavior and preference towards different promotional content based on gender schemas. Our results find that gender dynamics are complex. On the one hand, our findings show that female shoppers are more likely to respond to gaze cues and notice place-based advertising if others are also looking at the ad. On the other hand, male shoppers display longer staying and fixation times than females. Although a few detailed results are mixed, in our additional investigation, we found that gender is still a key factor in explaining the initial visual attention to promotional content within place-based advertising.

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References

  1. Alwall, N., Johansson, D., & Hansen, S. (2010). The gender difference in gaze-cueing: Associations with empathizing and systemizing. Personality and Individual Differences, 49(7), 729–732.

    Article  Google Scholar 

  2. Angell, R., Gorton, M., Sauer, J., Bottomley, P., & White, J. (2016). Don’t distract me when I’m media multitasking: Toward a theory for raising advertising recall and recognition. Journal of Advertising, 45(2), 198–210.

    Article  Google Scholar 

  3. Baker, J., & Wakefield, K. L. (2012). How consumer shopping orientation influences perceived crowding, excitement, and stress at the mall. Journal of the Academy of Marketing Science, 40(6), 791–806.

    Article  Google Scholar 

  4. Bakir, A., & Palan, K. M. (2010). How are children’s attitudes toward ads and brands affected by gender-related content in advertising? Journal of Advertising, 39(1), 35–48.

    Article  Google Scholar 

  5. Bayliss, A. P., di Pellegrino, G., & Tipper, S. P. (2005). Sex differences in eye gaze and symbolic cueing of attention. The Quarterly Journal of Experimental Psychology, 58A(4), 631–650.

    Article  Google Scholar 

  6. Bem, S. L. (1981). Gender schema theory: A cognitive account of sex typing. Psychological Review, 88(4), 354–364.

    Article  Google Scholar 

  7. Brunel, F. F., & Nelson, M. R. (2003). Message order effects and gender differences in advertising persuasion. Journal of Advertising Research, 43(3), 330–341.

    Google Scholar 

  8. Brunton, S. L., & Kutz, J. N. (2019). Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  9. Cai, Z., Fan, X., & Du, J. (2017). Gender and Attitudes toward technology use: A meta-analysis. Computers and Education, 105, 1–13.

    Article  Google Scholar 

  10. Chang, C. (2006). The Influence of masculinity and femininity in different advertising processing contexts: An accessibility perspective. Sex Roles, 55(5/6), 345–356.

    Article  Google Scholar 

  11. Chellappa, R., Wilson, C. L., & Sirohey, S. (1995). Human and machine recognition of faces—A survey. Proceedings of the IEEE, 83(5), 705–740.

    Article  Google Scholar 

  12. Chen, X., & Zelinsky, G. J. (2006). Real-world visual search is dominated by top-down guidance. Vision Research, 46(24), 4118–4133.

    Article  Google Scholar 

  13. Cheruvelil, K. S., & Soranno, P. A. (2018). Data-intensive ecological research is catalyzed by open science and team science. BioScience, 68(10), 813–822.

    Article  Google Scholar 

  14. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews: Neuroscience, 3(3), 201–215.

    Article  Google Scholar 

  15. Dalmaso, M., Galfano, G., Coricelli, C., & Castelli, L. (2014). Temporal dynamics underlying the modulation of social status on social attention. Plos ONE, 9, e93139.

    Article  Google Scholar 

  16. Dalmaso, M., Castelli, L., & Galfano, G. (2020). Social modulators of gaze-mediated orienting of attention: A review. Psychonomic Bulletin & Review, 27, 833–855.

    Article  Google Scholar 

  17. Darley, W. K., & Smith, R. E. (1995). Gender differences in message processing strategies: An empirical test of the selectivity model in advertising response. Journal of Advertising, 24(1), 41–56.

    Article  Google Scholar 

  18. Deaner, R. O., Shepherd, S. V., & Platt, M. L. (2007). Familiarity accentuates gaze cuing in women but not men. Biology Letters, 3, 64–67.

    Article  Google Scholar 

  19. van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato Mr. roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of Service Research, 20(1), 43–58.

    Article  Google Scholar 

  20. Edwards, S. G., Stephenson, L. J., Dalmaso, M., & Bayliss, A. P. (2015). Social orienting in gaze leading: A mechanism for shared attention. Proceedings of the Royal Society of London: Series B Biological Sciences, 282(1812), 1–8.

    Google Scholar 

  21. Eimer, M., Kiss, M., Press, C., & Sauter, D. (2009). The roles of feature-specific task set and bottom-up salience in attentional capture: An ERP study. Journal of Experimental Psychology: Human Perception and Performance, 35(5), 1316–1328.

    Google Scholar 

  22. Eroglu, S. A., & Machleit, K. A. (1990). An empirical study of retail crowding: Antecedents and consequences. Journal of Retailing, 66(2), 201–221.

    Google Scholar 

  23. Fortenberry, J. L., & McGoldrick, P. J. (2020). Do billboard advertisements drive customer retention? Expanding the ‘AIDA’ model to ‘AIDAR.’ Journal of Advertising Research, 60(2), 135–147.

    Article  Google Scholar 

  24. Frischen, A., Bayliss, A. P., & Tipper, S. P. (2007). Gaze cueing of attention: Visual attention, social cognition, and individual differences. Psychological Bulletin, 133(4), 694–724.

    Article  Google Scholar 

  25. Fugate, D. L., & Phillips, J. (2010). Product gender perceptions and antecedents of product gender congruence. Journal of Consumer Marketing, 27(3), 251–261.

    Article  Google Scholar 

  26. Gallup, A. C., Hale, J. J., Sumpter, D. J. T., Garnier, S., Kacelnik, A., Krebs, J. R., & Couzin, I. D. (2012). Visual attention and the acquisition of information in human crowds. Proceedings of the National Academy of Sciences of the United States of America, 109(19), 7245–7250.

    Article  Google Scholar 

  27. Goodrich, K. (2014). The gender gap: Brain-processing differences between the sexes shape attitudes about online advertising. Journal of Advertising Research, 54(1), 32–43.

    Article  Google Scholar 

  28. Hu, H., & Jasper, C. R. (2004). Men and women: A comparison of shopping mall behavior. Journal of Shopping Center Research, 11(1–2), 113–132.

    Google Scholar 

  29. Hult, G. T. M., Sharma, P. N., Morgeson, F. V., III., & Zhang, Y. (2019). Antecedents and consequences of customer satisfaction: Do they differ across online and offline purchases? Journal of Retailing, 95(1), 10–23.

    Article  Google Scholar 

  30. Igarashi, Y., Nagata, K., Kuwatani, T., Omori, T., Nakanishi-Ohno, Y., & Okada, M. (2016). Three levels of data-driven science. Journal of Physics: Conference Series, 699(1), 01200.

    Google Scholar 

  31. Itti, L. (2005). Quantifying the contribution of low-level saliency to human eye movements in dynamic scenes. Visual Cognition, 12(6), 1093–1123.

    Article  Google Scholar 

  32. Jain, A. K., & Li, S. Z. (2011). Handbook of face recognition. New York: Springer.

    Google Scholar 

  33. Khan, K., Attique, M., Syed, I., & Gul, A. (2019). Automatic gender classification through face segmentation. Symmetry, 11(6), 770–783.

    Article  Google Scholar 

  34. Kitchin, R. (2014). Big data, New epistemologies and paradigm shifts. Big Data and Society, 1(1), 1–12.

    Article  Google Scholar 

  35. Larivière, B., Bowen, D., Andreassen, T. W., Kunz, W., Sirianni, N. J., Voss, C., Wünderlich, N. V., & De Keyser, A. (2017). Service Encounter 2.0’: An investigation into the roles of technology, employees and customers. Journal of Business Research, 79, 238–246.

    Article  Google Scholar 

  36. Mandl, K. D., & Bourgeois, F. T. (2017). The evolution of patient diagnosis: From art to digital data-driven science. JAMA, 318(19), 1859–1860.

    Article  Google Scholar 

  37. Merritt, P., Hirshman, E., Wharton, W., Stangl, B., Devlin, J., & Lenz, A. (2007). Evidence of gender differences in visual selection attention. Personality and Individual Differences, 43, 597–609.

    Article  Google Scholar 

  38. Meyers-Levy, J. (1989). Gender differences in information processing: A selectivity interpretation. In P. Cafferata & A. M. Tybout (Eds.), Cognitive and affective responses to advertising. Lexington, MA: Lexington Books.

    Google Scholar 

  39. Milner, L. M., & Fodness, D. (1996). Product gender perceptions: The case of China. International Marketing Review, 13(4), 40–51.

    Article  Google Scholar 

  40. Milosavljevic, M., & Cerf, M. (2008). First attention then intention: Insights from computational neuroscience of vision. International Journal of Advertising, 27(3), 381–398.

    Article  Google Scholar 

  41. Mitchell, V., & Walsh, G. (2004). Gender differences in German consumer decision-making styles. Journal of Consumer Behavior, 3(4), 331–346.

    Article  Google Scholar 

  42. Orús, C., Gurrea, R., & Flavián, C. (2017). Facilitating imaginations through online product presentation videos: Effects on imagery fluency, product attitude and purchase intention. Electronic Commerce Research, 17(4), 661–700.

    Article  Google Scholar 

  43. Out of Home Advertising Association of America. (2020). OOH Revenue by Format. Retrieved from https://oaaa.org/AboutOOH/Factsamp;Figures/OOHRevenuebyFormat.aspx.

  44. Out of Home Advertising Association of America. (2019). OOH Market Share. (March 15). Retrieved from https://oaaa.org/AboutOOH/Factsamp;Figures/OOHMarketShare.aspx.

  45. Parkhurst, D., Law, K., & Niebur, E. (2002). Modeling the role of salience in the allocation of overt visual attention. Vision Research, 42(1), 107–123.

    Article  Google Scholar 

  46. Posner, M. I., & DiGirolamo, G. J. (1998). Executive attention: Conflict, target detection and cognitive control. In R. Parasuraman (Ed.), The attentive brain. Cambridge, MA: MIT Press.

    Google Scholar 

  47. Posner, M. I., & Petersen, S. E. (1990). The Attention system of the human brain. Annual Review of Neuroscience, 13, 25–42.

    Article  Google Scholar 

  48. Rokach, L., & Maimon, O. Z. (2008). Data mining with decision trees: Theory and applications (Vol. 69). Singapore: World Scientific.

    Google Scholar 

  49. Ruppert, E. (2013). Rethinking empirical social sciences. Dialogues in Human Geography, 3(3), 268–273.

    Article  Google Scholar 

  50. Shih, W., & Chai, S. (2016). Data-driven vs. hypothesis-driven research: Making sense of big data. In Academy of management proceedings (p. 14843). Briarcliff Manor, NY: Academy of Management.

  51. Stockdale, J. E. (1978). Crowding: Determinants and effects. Advances in Experimental Social Psychology, 11, 197–247.

    Article  Google Scholar 

  52. Sußenbach, F., & Schönbrodt, F. (2014). Not afraid to trust you: Trustworthiness moderates gaze cueing but not in highly anxious participants. Journal of Cognitive Psychology, 26, 670–678.

    Article  Google Scholar 

  53. Suh, T., Ford, J., Ryu, Y. S., & Kim, J. H. (2017). Enhancing the simultaneous utilization of measure in product design for academic-practitioner collaboration. Journal of Product and Brand Management., 26(3), 312–326.

    Article  Google Scholar 

  54. Suh, T., Kang, S., & Kemp, E. A. (2018). A Bayesian network approach to juxtapose brand engagement and behaviors of substantive interest in e-services. Electronic Commerce Research, 20, 361–379.

    Article  Google Scholar 

  55. Taylor, C. R. (2012). Editorial: Back to the future: Some topics we should not forget about in advertising research. International Journal of Advertising, 31(4), 699–702.

    Article  Google Scholar 

  56. Thomas-Smith, C., & Barnett, G. (2010). Seeing is believing: Viewing engagement in place-based media. In D. S. Fellows (Ed.), Your audience = media consumer + generator. Amsterdam: ESOMAR.

    Google Scholar 

  57. van Eeden, J. (2006). The gender of shopping malls. Communication, 32(1), 38–64.

    Google Scholar 

  58. Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: Introduction to the special issue on multi-channel retailing. Journal of retailing, 91(2), 174–181.

    Article  Google Scholar 

  59. Wilson, R. T., Baack, D. W., & Till, B. D. (2015). Creativity, attention, and the memory for brands: An outdoor advertising field study. International Journal of Advertising, 34(2), 232–261.

    Article  Google Scholar 

  60. Wilson, R. T., & Casper, J. (2016). The Role of location and visual salience in capturing attention to outdoor advertising. Journal of Advertising Research, 56(3), 259–273.

    Article  Google Scholar 

  61. Wilson, R. T., & Suh, T. (2018). Advertising to the masses: The effects of crowding on the attention to place-based advertising. International Journal of Advertising, 37(3), 402–420.

    Article  Google Scholar 

  62. Wilson, R. T., & Till, B. D. (2019). Managing advertising in non-traditional environments: A message processing framework. In S. Rodgers & E. Thorson (Eds.), Advertising theory (2nd ed.). New York, NY: Routledge, Taylor & Francis Group.

    Google Scholar 

  63. Wilson, R. T., & Till, B. D. (2013). Recall of preshow cinema advertising: A message processing perspective. Journal of Marketing Communications, 19(1), 1–21.

    Article  Google Scholar 

  64. Wilson, R. T., & Till, B. D. (2008). Airport advertising effectiveness: An exploratory field study. Journal of Advertising, 37(1), 59–72.

    Article  Google Scholar 

  65. Wolfe, J. M. (1994). Visual search 20: A revised model of visual search. Psychonomic Bulletin and Review, 1(2), 202–238.

    Article  Google Scholar 

  66. Wolfe, J. M. (1998). Visual search. In H. Pashler (Ed.), Attention. Hove: Psychology Press.

    Google Scholar 

  67. Wolin, L. D. (2003). Gender issues in advertising—An oversight synthesis of research: 1970–2002. Journal of Advertising Research, 43(1), 111–129.

    Article  Google Scholar 

  68. Yarbus, A. L. (1967). Eye Movements and Vision. New York, NY: Plenum.

    Book  Google Scholar 

  69. Zhao, W., Chellappa, R., Rosenfeld, A., & Phillips, P. J. (2003). Face recognition: A literature survey. ACM Computing Surveys, 35(4), 399–458.

    Article  Google Scholar 

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Authors and Affiliations

  1. McCoy College of Business, Texas State University, San Marcos, USA

    Taewon Suh & Rick T. Wilson

  2. Entrepreneurial Innovators Group, Seoul, South Korea

    Seungtae On

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  1. Taewon Suh
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Correspondence to Taewon Suh.

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Suh, T., Wilson, R.T. & On, S. Gender difference in visual attention to digital content of place-based advertising: a data-driven scientific approach. Electron Commer Res 23, 877–897 (2023). https://doi.org/10.1007/s10660-021-09494-9

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