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Previous Research and Theoretical Background

  • Lena Himbert
Chapter

Abstract

After presenting and categorizing the previous unit price literature in general, this chapter focuses specifically on the theoretical foundation of the hypotheses that are developed and tested in the next chapter. As previously outlined, this thesis addresses two sets of research questions. The first set of research questions is related to the influence of unit price format on the attitude towards products, while the second set of research questions addresses the influence of unit price availability and unit price prominence on the attitude towards retailers. In order to underline the relevance of these research questions, as well as to better understand the influence of the unit price on the judgment of products and retailers, an overview of previous findings in relevant fields of research is given. As the first research question addresses the influence of unit price format on price-level perception, an overview of previous research on the influence of framing on the perception of quantitative information is presented, as the unit price format can be seen as the framing of unit price information. The second research question addresses the influence of unit price format on quality perception and purchase intention. Therefore, also findings of previous literature that investigates the general influence of price on quality is highlighted highlight. As the second set of research questions (i.e., research questions 3 and 4) addresses the influence of unit prices on store price image, an overview of previous research on store price image is provided. Finally, after giving an overview of these fields of research, theories and consumer heuristics are presented that are utilized in Chapter 4 to develop the individual sets of hypothesis.

Keywords

Purchase Intention Reference Price Unit Price Quality Perception Unit Prex 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Fachmedien Wiesbaden 2016

Authors and Affiliations

  1. 1.Technische Universität KaiserslauternKaserslauternGermany

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