Skip to main content
SpringerLink
Log in

How do social-based cues influence consumers’ online purchase decisions? An event-related potential study

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

Product rating and sales are two important social-based cues in online shopping. This study applies the event-related potential (ERP) approach to explore the underlying neural mechanism of the joint influence of these two cues on consumers’ decision-making. Behavioral data show that product rating has a greater impact on the purchasing rate than sales, which positively moderates the latter’s effect and supports cue-diagnosticity theory. Electrophysiological data provide further explanations for the observed behavioral pattern. Analyses of main ERP components suggest that consumers go through a series of cognitive processes from processing of perceived risk (N2) and informational conflict (N400) to evaluative categorization (LPP) before making the final purchasing decision. Specifically, product rating significantly influences the risk perception while the combination of high rating and low sales elicits significant cognitive conflict. Both cues are adopted by consumers to make an overall evaluation based on their similarity to the criterion.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Notes

  1. Due to equipment characteristics and time considerations, ERP and other neuroscience approaches typically involve multiple trials (e.g., 320 trials in the present study) with a relatively small number of subjects [13, 28].

  2. We bought the corresponding headphones after the whole experiment was completed and then sent the headphones to the “lucky” subjects.

  3. The interval lasted for 2 min on average.

  4. -200 ms means 200 ms before the onset of the stimulus.

References

  1. Aiken, K. D., & Boush, D. M. (2006). Objective-source ratings, and implied investments in advertising: Investigating online trust and the context-specific nature of internet signals. Journal of the Academy of Marketing Science, 34(3), 308–323.

    Article  Google Scholar 

  2. Akdeniz, B., Calantone, R. J., & Voorhees, C. M. (2013). Effectiveness of marketing cues on consumer perceptions of quality: The moderating roles of brand reputation and third-party information. Psychology & Marketing, 30(1), 76–89.

    Article  Google Scholar 

  3. Andrews, J. C., Durvasula, S., & Akhter, S. H. (1990). A framework for conceptualizing and measuring the involvement construct in advertising research. Journal of Advertising, 19(4), 27–40.

    Article  Google Scholar 

  4. Azizian, A., Freitas, A. L., Watson, T. D., & Squires, N. K. (2006). Electrophysiological correlates of categorization: P300 amplitude as index of target similarity. Biological Psychology, 71, 278–288.

    Article  Google Scholar 

  5. Cacioppo, J. T., Crites, S. L., Gardner, W. L., & Bernston, G. G. (1994). Bioelectrical echoes from evaluative categorizations: a late positive brain potential that varies as a function of trait negativity and extremity. Journal of Personality and Social Psychology, 67, 115–125.

    Article  Google Scholar 

  6. Carretié, L., Mercado, F., Tapia, M., & Hinojosa, J. A. (2001). Emotion, attention and the “negativity bias”, studied through event-related potentials. International Journal of Psychophysiology, 41(1), 75–85.

    Article  Google Scholar 

  7. Chen, M., Ma, Q., Li, M., Dai, S., Wang, X., & Shu, L. (2010). The neural and psychological basis of herding in purchasing books online: An event-related potential study. CyberPsychology, Behavior & Social Networking, 13(3), 321–328.

    Article  Google Scholar 

  8. Chen, M., Ma, Q., Li, M., Lai, H., Wang, X., & Shu, L. (2010). Cognitive and emotional conflicts of counter-conformity choice in purchasing books online: An event-related potentials study. Biological Psychology, 85(3), 437–445.

    Article  Google Scholar 

  9. Chen, Y. F. (2008). Herd behavior in purchasing books online. Computers in Human Behavior, 24(5), 1977–1992.

    Article  Google Scholar 

  10. Chen, Y. F. (2011). Auction fever: Exploring informational social influences on bidder choices. CyberPsychology, Behavior & Social Networking, 14(7), 411–416.

    Article  Google Scholar 

  11. Cowen, L., Ball, L., & Delin, J. (2002). An eye movement analysis of webpage usability. Interface, 28(1), 317–335.

    Google Scholar 

  12. Cox, D. F. (1967). The sorting rule model of the consumer product evaluation process. In D. F. Cox (Ed.), Risk taking and information handling in consumer behavior (pp. 324–369). Boston, MA: Graduate school of Business Administration, Harvard University.

    Google Scholar 

  13. Dimoka, A., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Gefen, D., et al. (2012). On the use of neurophysiological tools in IS Research: Developing a research agenda for neuroIS. MIS Quarterly, 36(3), 679–702.

    Google Scholar 

  14. Folstein, J. R., & Van Petten, C. (2008). Influence of cognitive control and mismatch on the N2 component of the ERP: A review. Psychophysiology, 45, 152–170.

    Article  Google Scholar 

  15. Forster, S. E., Carter, C. S., Cohen, J. D., & Cho, R. Y. (2010). Parametric manipulation of the conflict signaland control-state adaptation. Journal of Cognitive Neuroscience, 23(4), 923–935.

    Article  Google Scholar 

  16. Frishkoff, G. A., TuckeL, D. M., & Davey, C. (2004). Frontal and posterior sources of event-related potentials in semantic comprehension. Cognitive Brain Research, 20(3), 329–354.

    Article  Google Scholar 

  17. Fugate, D. L. (2007). Neuromarketing: A layman’s look at neuroscience and its potential application to marketing practice. Journal of Consumer Marketing, 24(7), 385–394.

    Article  Google Scholar 

  18. Gidron, D., Koehler, D. J., & Tversky, A. (1993). Implicit quantification of personality traits. Personality and Social Psychology Bulletin, 19, 594–604.

    Article  Google Scholar 

  19. Hajcak, G., & Olvet, D. M. (2008). The persistence of attention to emotion: Brain potentials during and after picture presentation. Emotion, 8, 250–255.

    Article  Google Scholar 

  20. Herring, D. R., Taylor, J. H., White, K. R., & Crites, S. L. (2011). Electrophysiological responses to evaluative priming: The LPP is sensitive to incongruity. Emotion, 11(4), 794–806.

    Article  Google Scholar 

  21. Hu, X., Wu, G., Wu, Y., & Zhang, H. (2010). The effects of Web assurance seals on consumers’ initial trust in an online vendor: A functional perspective. Decision Support Systems, 48, 407–418.

    Article  Google Scholar 

  22. Huang, J. H., & Chen, Y. F. (2006). Herding in online product choice. Psychology and Marketing, 23(5), 413–428.

    Article  Google Scholar 

  23. Huang, Y., Kendrick, K. M., & Yu, R. J. (2014). Social conflicts elicit an N400-like component. Neuropsychologia, 65, 211–220.

    Article  Google Scholar 

  24. Ito, T. A., & Cacioppo, J. T. (2000). Electrophysiological evidence of implicit and explicit categorization processes. Journal of Experimental Social Psychology, 36, 660–676.

    Article  Google Scholar 

  25. Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). The endowment effect, loss aversion, and status quo bias. The Journal of Economic Perspectives, 5(1), 193–206.

    Article  Google Scholar 

  26. Kassarjian, H. H. (1971). Personality and consumer behavior: A review. Journal of Marketing Research, 8(4), 409–418.

    Article  Google Scholar 

  27. Kim, D., & Benbasat, I. (2009). Trust-assuring arguments in B2C e-commerce: Impact of content, source, and price on trust. Journal of Management Information Systems, 26(3), 175–206.

    Article  Google Scholar 

  28. Kuan, Kevin K. Y., Zhong, Y. Q., & Chau, Patrick Y. K. (2014). Informational and normative social influence in group-buying: Evidence from self-reported and EEG data. Journal of Management Information Systems, 30(4), 151–178.

    Article  Google Scholar 

  29. Kumar, N., & Benbasat, I. (2006). The influence of recommendations and consumer reviews on evaluations of websites. Information Systems Research, 17(4), 425–439.

    Article  Google Scholar 

  30. Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology, 62, 621–647.

    Article  Google Scholar 

  31. Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect semantic incongruity. Science, 207(4427), 203–205.

    Article  Google Scholar 

  32. Langeslag, S. J. E., Jansma, B. M., Franken, I. H. A., & Strien, J. W. V. (2007). Event related potential responses to love-related facial stimuli. Biological Psychology, 76, 109–115.

    Article  Google Scholar 

  33. Larson, M. J., Clayson, P. E., & Baldwin, S. A. (2012). Performance monitoring following conflict: Internal adjustments in cognitive control? Neuropsychologia, 50, 426–433.

    Article  Google Scholar 

  34. Lee, J., Park, D. H., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Research and Applications, 7(3), 341–352.

    Article  Google Scholar 

  35. Leuthold, S., Schmutz, P., Bargas-Avila, J. A., Tuch, A. N., & Klaus, O. (2011). Vertical versus dynamic menus on the world wide web: eye tracking study measuring the influence of menu design and task complexity on user performance and subjective preference. Computers in Human Behavior, 27(1), 459–472.

    Article  Google Scholar 

  36. Luck, S. J. (2005). An introduction to the event-related potential technique (cognitive neuroscience). Cambridge, MA: MIT Press.

    Google Scholar 

  37. Ma, Q. G., Meng, L., Zhang, Z. X., Xu, Q., Wang, Y., & Shen, Q. (2015). You did not mean it: Perceived good intentions alleviate sense of unfairness. International Journal of Psychophysiology, 96(3), 183–190.

    Article  Google Scholar 

  38. Ma, Q. G., Pei, G. X., & Wang, K. (2015). Influence of negative emotion on the framing effect: Evidence from event-related potentials. NeuroReport, 26(6), 325–332.

    Article  Google Scholar 

  39. Ma, Q. G., Wang, X. Y., Shu, L. C., & Dai, S. Y. (2008). P300 and categorization in brand extension. Neuroscience Letter, 431, 57–61.

    Article  Google Scholar 

  40. Ma, Q. G., Wang, C. C., Wu, Y. F., & Wang, X. Y. (2014). Event-related potentials show taste and risk effects on food evaluation. NeuroReport, 25, 760–765.

    Article  Google Scholar 

  41. Marchand, Y., Inglis-Assaff, P. C., & Lefebvre, C. D. (2013). Impact of stimulus similarity between the probe and the irrelevant items during a card-playing deception detection task: The “irrelevants” are not irrelevant. Journal of Clinical and Experimental Neuropsychology, 35(7), 686–701.

    Article  Google Scholar 

  42. 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 

  43. Näätänen, R. (1990). The role of attention in auditory information processing as revealed by event-related potentials and other brain measures of cognitive function. Behavioral and Brain Sciences, 13, 201–288.

    Article  Google Scholar 

  44. Novemsky, N., & Kahneman, D. (2005). The boundaries of loss aversion. Journal of Marketing Research, 42(2), 119–128.

    Article  Google Scholar 

  45. Olson, J. C. (1972). Cue utilization in the quality perception process: a cognitive model and an empirical test. Dissertation for the degree of Doctor, Purdue University.

  46. Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009). The influence of website characteristics on a consumer’s urge to buy impulsively. Information Systems Research, 20(1), 60–78.

    Article  Google Scholar 

  47. Pavlou, P. A., Liang, H. G., & Xue, Y. J. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31(1), 105–136.

    Google Scholar 

  48. Petty, R., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10, 135–146.

    Article  Google Scholar 

  49. Picton, T. W. (1992). The P300 wave of the human event-related potential. Journal Of clinical of Neurophysiology, 9, 456–479.

    Article  Google Scholar 

  50. Purohit, D., & Srivastava, J. (2001). Effect of manufacturer reputation, retailer reputation, and product warranty on consumer judgments of product quality: A cue diagnosticity framework. Journal of Consumer Psychology, 10, 123–134.

    Article  Google Scholar 

  51. Ruyter, K. D., Wetzels, M., & Kleijnen, M. (2001). Customer adoption of e-service: An experimental study. International Journal of Service Industry Management, 12(2), 184–207.

    Article  Google Scholar 

  52. SanJosé-Cabezudo, R., Gutiérrez-Arranz, A. M., & Gutiérrez-Cillán, J. (2009). The combined influence of central and peripheral routes in the online persuasion process. CyberPsychology & Behavior, 12(3), 299–308.

    Article  Google Scholar 

  53. San-Martín, S., & Camarero, C. (2012). A cross-national study on online consumer perceptions, trust, and loyalty. Journal of Organizational Computing & Electronic Commerce, 22(1), 64–86.

    Article  Google Scholar 

  54. Skowronski, J. J., & Carlston, D. E. (1987). Social judgment and social memory: The role of cue diagnosticity in negativity, positivity, and extremity biases. Journal of Personality and Social Psychology, 52, 689–699.

    Article  Google Scholar 

  55. Slovic, P., & Lichtenstein, S. (1971). Comparison of Bayesian and regression approaches to the study of information processing in judgment. Organizational Behavior and Human Performance, 6, 649–774.

    Article  Google Scholar 

  56. Spapé, M. M., Band, G. P. H., & Hommel, B. (2011). Compatibility-sequence effects in the Simon task reflect episodic retrieval but not conflict adaptation: Evidence from LRP and N2. Biological Psychology, 88, 116–123.

    Article  Google Scholar 

  57. Steffensen, S. C., Ohran, A. J., Shipp, D. N., Hales, K., Stobbs, S. H., & Fleming, D. E. (2008). Gender-selective effects of the P300 and N400 components of the visual evoked potential. Vision Research, 48(7), 917–925.

    Article  Google Scholar 

  58. Suki, N. M. (2013). Consumer shopping behaviour on the Internet: Insights from Malaysia. Electronic Commerce Research, 13, 477–491.

    Article  Google Scholar 

  59. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.

    Article  Google Scholar 

  60. Utz, S., Kerkhof, P., & van den Bos, J. (2011). Consumers rule: How consumer reviews influence perceived trustworthiness of online stores. Electronic Commerce Research and Applications, 11(1), 49–58.

    Article  Google Scholar 

  61. Walsh, M. M., & Anderson, J. R. (2012). Learning from experience: Event-related potential correlates of reward processing, neural adaptation, and behavioral choice. Neuroscience and Biobehavioral Reviews, 36, 1844–1870.

    Article  Google Scholar 

  62. Wells, J. D., Valacich, J. S., & Hess, T. J. (2011). What signal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS Quarterly, 35(2), 373–396.

    Google Scholar 

  63. Wu, Y., Zhou, Y. Q., van Dijk, E., Leliveld, M. C., & Zhou, X. L. (2011). Social comparison affects brain responses to fairness in asset division: An ERP study with the ultimatum game. Frontiers in Human Neuroscience, 5, 131.

    Article  Google Scholar 

  64. Yang, J., Li, H., Zhang, Y., Qiu, J., & Zhang, Q. L. (2007). The neural basis of risk decision-making in a blackjack task. NeuroReport, 18(14), 1507–1510.

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by the grant from National Natural Science Foundation of China (No: 71272167, No: 71202157) and Ministry of Education of the People’s Republic of China (No: 11YJA630130). The authors would like to thank Qian Yao and Jun Bian for their valuable inputs on earlier versions of this article.

Author information

Authors and Affiliations

  1. School of Management, Zhejiang University, Hangzhou, People’s Republic of China

    Qiuzhen Wang, Liang Meng & Qingguo Ma

  2. Saunders College of Business, Rochester Institute of Technology, Rochester, NY, USA

    Manlu Liu

  3. NetEase, Inc, Hangzhou, People’s Republic of China

    Qi Wang

Authors
  1. Qiuzhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manlu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qingguo Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manlu Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Meng, L., Liu, M. et al. How do social-based cues influence consumers’ online purchase decisions? An event-related potential study. Electron Commer Res 16, 1–26 (2016). https://doi.org/10.1007/s10660-015-9209-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-015-9209-0

Keywords

Search

Navigation