Research of IT influence on the price perception


Abstract

Introduction. The study contributes to the theoretical knowledge by expanding understanding of auditory encoding of prices, further testing the working memory capacities, and understanding the psychological underpinnings of price perceptions. From a managerial perspective, our findings will help marketers to better understand the cognitive processes of price perception while voice-ordering through smart devices, thus improving company pricing decisions and increasing number of sales.

Aim and tasks. In this study, we aim to understand the psychological underpinnings of price perception during “auditory” price information encoding. In particular, we research how the price pronunciation order of the item on sale (first the sale price and then the usual price or vice versa) affects the sale evaluation and subsequent purchase intention.

Results. Prior to making predictions about price perception through auditory sense and its subsequent evaluation, we need to understand the cognitive processes underlying numbers encoding. Numerical cognition process follows five stages: (1) initial exposure to numerical information (i.e., numerical presentation in visual or verbal format), (2) numerical information encoding, (3) representation of the numerical information in memory, (4) retrieval of that information in order to perform some cognitive task (e.g. price evaluation), and (5) consumer response based on processed information. Thus, the internal consistency reliability of the questions has already been tested using Cronbach’s alpha parameter and has been proved to be of the appropriate level. Lastly, in addition to these context-related questions, we include two attention checks questions and the question on the questionnaire purpose in order to control for random box-checking and exclude responses which guessed the study reasons from further analysis.

Conclusions. From a theoretical standpoint, this study contributes to two literature streams:
(1) marketing literature on pricing and (2) the psychological literature on numerical cognition. In the pricing area, the findings of the study further support and shed light on the application of the anchoring effect during purchase decisions. The study taps into the area of conscious and unconscious comparisons with price anchors and helps to reconcile previous researches who found different effects of price anchors on willingness to pay for the product or service. In addition, the study provides novel insights regarding pricing decisions in “auditory” rather than “visual” domain, laying a foundation for further exploration of this area.

Keywords:

IT marketing, price perception, IOT, smart devices, anchoring.

References

1. Adaval, R., & Monroe, K. B. (2002). Automatic construction and use of contextual information for product and price evaluations. Journal of Consumer Research, 28(4), 572-588.
2. Adaval, R., & Wyer, R. S. (2011). Conscious and Nonconscious Comparisons with Price anchors: effects on Willingness to Pay for Related and Unrelated Products. Journal of Markering Research, XLVIII, 355-365.
3. Alford, B. L., & Biswas, A. (2002). The effects of discount level, price consciousness and sale proneness on consumers' price perception and behavioral intention. Journal of Business Research, 55(9), 775-783.
4. Ariely, D., Loewenstein, G., & Prelec, D. (2003). “Coherent Arbitrariness”: Stable Demand Curves Without Stable Preferences. The Quarterly Journal of Economics, 118(1), 73-106.
5. Ashcraft, M. H. (1992). Cognitive arithmetic: A review of data and theory. Cognition, 22(1-2), 75-106.
6. Baddeley, A. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Sciences, 4, 417-422.
7. Baddeley, A. D. (1992). Working memory. Science, 255(5054), 556-559.
8. Baddeley, A. D. (2001). Is Working Memory Still Working? American Psychologist, 56, 851-864.
9. Baddeley, A. D., & Hitch, G. (1974). Working Memory. Psychology of Learning and Motivation, 8, 47-89.
10. Barbera, M., Northey, G., Septianto, F., & Spanjaard, D. (2018). Those prices are HOT! How temperature-related visual cues anchor expectations of price and value. Journal of Retailing and Consumer Services, 44, 178-181.
11. Biswas, A., Bhowmick, S., Guha, A., & Grewal, D. (2013). Consumer Evaluations of Sale Prices: Role of the Subtraction Principle. Journal of Marketing, 77, 49-66.
12. Blair, E. A., & Landon Jr, L. E. (1981). The Effect if Reference Prices in Retail Advertisement. Journal of Marketing, 45, 61-69.
13. Coulter, K. S., & Coulter, R. A. (2010). Small sounds, big deals: Phonetic symbolism effects in pricing. Journal of Consumer Research, 37(2), 315-328.
14. Coulter, K. S., Choi, P., & Monroe, K. B. (2012). Comma N' cents in pricing: The effects of auditory representation encoding on price magnitude perceptions. Journal of Consumer Psychology, 22, 395-407.
15. Davis, D. F., & Bagchi, R. (2018). How Evaluations of Multiple Percentage Price Changes Are Influenced by Presentation Mode and Percentage Ordering: The Role of Anchoring and Surprise. Journal of Marketing Research, 55(5), 655-666.
16. Dawar, N., & Bendle, N. (2018, May-June). Marketing in the Age of Alexa. Harvard Business Review.
17. Dehaene, S. (1992). Varieties of numerical abilities. Cognition, 44(1-2), 1-42.
18. Dehaene, S., Dehaene-Lambertz, G., & Cohen, L. (1998). Abstract representations of numbers in the animal and human brain. Trends in Neuroscience, 21(8), 355-361.
19. Englich, B. (2006). Blind or Biased? Justitia's Susceptibility to Anchoring Effects in the Courtroom Based on Given Numerical Representations. Law & Policy, 28(4), 497-514.
20. Epley, N., & Gilovich, T. (2010). Anchoring Unbound. Journal of Consumer Psychology, 20(1), 20-24.
21. Gollnhofer, J. F., & Schüller, S. (2018). Sensing the Vocal Age. Managing Voice Touchpoints on Alexa. Marketing Review St Gallen, 888-897.
22. Grewal, D., Marmorstein, H., & Sharma, A. (1996). Communicating Price Information through Semantic Cues: The Moderating Effects of Situation and Discount Size. Journal of Consumer Research, 23, 148-155.
23. Grewal, D., Roggeveen, A. L., & Nordfält, J. (2017). The Future of Retailing. Jounal of Retailing, 93(1), 1-6.
24. Jordan, E. (2019). The Future of Retail. Retrieved from Walker Sands: https://1bnznaaikg11oqsn3tvx88r9-wpengine.netdna-ssl.com/wp-content/uploads/2019/09/WalkerSands_Future_of_B2B_Retail_2019_WSRB_FINAL.pdf
25. Kahn, B. (2017). Using Visual Design to Improve Customer Perceptions of Online Assortments. Journal of Retailing, 93(1), 29-42.
26. Lalot, F., Quiamzade, A., & Falomir‐Pichastor, J. (2019). How many migrants are people willing to welcome into their country? The effect of numerical anchoring on migrants' acceptance. Journal of Applied Social Psychology, 49(6), 361-371.
27. Luna, D., & Kim, H. M. (2009). How much was your shopping basket? Working memory processes in total basket price estimation. Journal of Consumer Psychology, 19, 346-355.
28. Malhotra, N. K., Nunan, D., & Birks, D. F. (2017). Marketing Research. An applied approach (Fifth Edition). Harlow: Pearson Education Limited.
29. Milosavljevic, M., Navalpakkam, V., Koch, C., & Rangel, A. (2012). Relative Visual Salience Differences Induce Sizable Bias in Consumer Choice. Journal of Consumer Psychology, 22(1), 67-74.
30. Quattrone, G. A., Lawrence, C. P., Finkel, S. E., & Andrus, C. D. (1984). Explorations in Anchoring: The Effects of Prior Range, Anchor Extremity, and Suggestive Hints. Stanford University.
31. Ruy, K., & Han, H. (2011). New or repeat customers: How does physical environment influence their restaurant experience. International Journal of Hospitability Management, 30(3), 599-611.
32. Saini, R., & Thota, S. C. (2010). The psychological underpinnings of relative thinking in price comparisons. Journal of Consumer Psychology, 20(2), 185-192.
33. Smith, S. (2018). Digital Voice Assistants in use to triple to 8 billion by 2023, driven by smart home devices. Press Relations. Retrieved from: https://www.juniperresearch.com
34. SurveyMonkey (2020). Sample Size Calculator. Retrieved from https://www.surveymonkey.com.
35. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.
36. Vanhuele, M., Laurent, G., & Dreze, X. (2006). Consumers' Immediate Memory for Prices. Journal of Consumer Research, 33, 163-172.
37. Verhoeven, J. W., Rompay, T. J., & Pruyn, A. (2009). The price facade: Symbolic and behavioral price cues in service environments. International Journal of Hospitality Management, 28(4), 604-611.
38. Yeshchenko, M., Koval, V., & Tsvirko, O. (2019). Economic policy priorities of the income regulation. Espacios, 40 (38), 11.
39. Perez, S. (2018, Mar 7). 47.3 million U.S. adults have access to a smart speaker, report says. Retrieved from Tech Crunch: https://techcrunch.com/2018/03/07/47-3-million-u-s-adults-have-access-to-a-smart-speaker-report-says.
Published
2020-06-12
How to Cite
(1)
Filipishina, L.; Gonchar, V.; Bohachov, O. Research of IT Influence on the Price Perception. Economics Ecology Socium 2020, 4, 40-51.