Abstract
Digital Marketing, and specifically, targeted marketing online is flourishing in recent years, and is becoming evermore precise and easy to implement, given the rise of big data and algorithmic processes. This study assesses users’ perceptions regarding the fairness in algorithmic targeted marketing, in conditions of scarcity. This is increasingly important because as more decisions are made by data-driven algorithms, the potential for consumers to be treated unfairly by marketers grows. Awareness of users’ perceptions helps to create a more open, understandable and fair digital world without negative influences. Also, it may help both marketers and consumers to communicate effectively.
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Notes
- 1.
Artificial intelligence systems focused on a singular or limited task. https://deepai.org/machine-learning-glossary-and-terms/narrow-ai.
References
Aggarwal, P., Jun, S.Y., Huh, J.H.: Scarcity messages. J. Advert. 40(3), 19–30 (2011)
Bayer, J.: Double harm to voters: data-driven micro-targeting and democratic public discourse. Internet Policy Rev. 9(1), 1–7 (2020)
Becker, M.: Privacy in the digital age: comparing and contrasting individual versus social approaches towards privacy. Ethics Inf. Technol. 21(4), 307–317 (2019). https://doi.org/10.1007/s10676-019-09508-z
Broeder, P., Derksen, R.: Exclusivity in online targeted promotions: cross-cultural preferences of consumers. Int. J. Bus. Emerg. Mark. 10(4), 396–408 (2018)
Brock. T.C.: Implications of commodity theory for value change. In: Psychological Foundations of Attitudes, pp. 243–275 (1968)
Cialdini, R.B.: Influence: Science and Practice, 4th edn. Allyn & Bacon (2008)
Fromkin, H.L.: Effects of experimentally aroused feelings of undistinctiveness upon valuation of scarce and novel experiences. J. Pers. Soc. Psych. 16(3), 521 (1970)
Gierl, H., Huettl, V.: Are scarce products always more attractive? The interaction of different types of scarcity signals with products’ suitability for conspicuous consumption. Int. J. Res. Mark. 27(3), 225–235 (2010)
Gupta, S., Melewar, T.C., Bourlakis, M.: A relational insight of brand personification in business-to-business markets. J. Gener. Manag. 35(4), 65–76 (2010)
Howard, P.: New Media Campaigns and the Managed Citizen (Communication, Society and Politics). Cambridge University Press, Cambridge (2006)
Kleinberg, J., Ludwig, J., Mullainathan, S., Sunstein, C.R.: Discrimination in the age of algorithms. J. Legal Anal. 10, 113–174 (2018)
Trinh Anh, K.: Scarcity effects on consumer purchase intention in the context of E-commerce. Aalto University (2014)
Lee, M.K.: Understanding perception of algorithmic decisions: fairness, trust, and emotion in response to algorithmic management. Big Data Soc. 5(1) (2018)
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., Vinck, P.: Fair, transparent, and accountable algorithmic decision-making processes. Philos. Technol. 31(4), 611–627 (2017). https://doi.org/10.1007/s13347-017-0279-x
Lynn, M.: Scarcity effects on value: a quantitative review of the commodity theory literature. Psychol. Mark. 8, 43–57 (1991)
Mittone, L., Savadori, L.: The scarcity bias. Appl. Psychol.: Int. Rev. 58(3), 453–468 (2009). Open J. Bus. Manag. 3, 96–108 (2015)
Mussomeli, A., Neier, M., Takayama, B., Sniderman, B., Holdowsky, J.: Building a cognitive digital supply network. Augmenting automation in an AI world. Deloitte Insights (2019)
O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, 1st edn. Crown, New York (2016)
Schumpe, B.M., Erb, H.P.: Humans and uniqueness. Sci. Prog. 98(1), 1–11 (2015)
Shi, X., Li, F., Chumnumpan, P.: The use of product scarcity in marketing. Eur. J. Mark. ahead-of-print (2020)
Shin, D., Park, Y.J.: Role of fairness, accountability, and transparency in algorithmic affordance. Comput. Hum. Behav. 98, 277–284 (2019)
The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, First Edition. IEEE (2019)
Wing, J.M.: Data for good: FATES, elaborated. Columbia University Data Science Institute, 23 January 2018
Winter, S., Maslowska, E., Vos, A.: The effects of trait-based personalization in social media advertising (2021)
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This project is partially funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 810105 (CyCAT).
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Pavlidou, V., Otterbacher, J., Kleanthous, S. (2022). User Perception of Algorithmic Digital Marketing in Conditions of Scarcity. In: Themistocleous, M., Papadaki, M. (eds) Information Systems. EMCIS 2021. Lecture Notes in Business Information Processing, vol 437. Springer, Cham. https://doi.org/10.1007/978-3-030-95947-0_22
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