 The Effects of User Tracking and Behavioral Management on Online Prices: A Theoretical ApproachChristian Niemeier and Richard Pospisil European Research Studies Journal, 2021, vol. XXIV, issue 3 - Part 2, 386-398 Abstract: Purpose: Only little is known about the technical aspect of dynamic, individual prices in various forms of online-shops as well as how exactly these prices are calculated. The aim of this article is to unveil the variables and patterns behind online dynamic pricing. Design/Methodology/Approach: A Software was witten to gather the necessary database in both, an experimental and non-experimental setting. In addion a statistical regression analysis was conducted to ensure data integrity, by reducing amplitudes and noise from the databasis. Findings: There is vast literature on the topic. Literature is outdated pretty fast, as technology is moving ahead of science finding. Variables such as the user origin, device type, on-page-behavior and eventual cookies from previous website visits rsp. Ads do matter in the finding of the price. In general it could be said, that prices on mobile devices are more dynamic than on desktop versions. Thus, buying on a mobile can either be way cheaper or way more expensive than on a desktop computer. Looking at the GPS data, speaking about the user origin, data shows that there could be a pattern proven, that “discriminates” some countries (e.g., Western-EU, USA) by favoring others, preferably lower-wage markets such as Eastern Europe (Croatia) or India. Practical Implications: The present results suggest how both, vendor and customer can optimize their setting, the data they share and the behavior they show, to optimize the price given in specific situation or on request level, based upon their individual pricing request. Originality/Value: The present study is one of the first studies in the economical framework that does not just list the variables existing, but also linking them together and scientifically prove patterns, as fast as available / statistical relevance could be given. Keywords: Price discrimination; price differentiation; dynamic pricing; online shopping. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:xxiv:y:2021:i:3-part2:p:386-398 Access Statistics for this article More articles in European Research Studies Journal from European Research Studies Journal |
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