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Marketing insights for mobile advertising and consumer segmentation in the cloud era: A Q–R hybrid methodology and practices

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  • Kim, Ki Youn
  • Lee, Bong Gyou

Abstract

The mobile advertising paradigm is shifting from the web2.0 to the web3.0 generation in the Korean market and pursuing a customized and context-aware advertisement service for each consumer in this cloud computing era. In the Korean telecommunication market, the expanded demand for smart devices and the heralding of the 4G mobile broadband networks have increased the use of mobile applications and web services, with strengthened competition among advertising industrial players. Recently, as the mobile ecosystem becomes more complex, advertisement marketers are focusing on targeted marketing to customers to maximize the impact of advertising. Mobile advertising businesses should differ in terms of content and delivery patterns as to what users want, as well as how they react to different smart devices and platforms. The purpose of this study is to discover and theorize customer typologies based on Q theory's subjectivity in a qualitative approach and then verify and generalize sequentially these theoretical definitions and concepts through a combination of the Q and R empirical methods. The results of this research can be used as an antecedent of theoretical and industrial frameworks and a basic statistical data in advertising marketing and customer relationship management domains.

Suggested Citation

  • Kim, Ki Youn & Lee, Bong Gyou, 2015. "Marketing insights for mobile advertising and consumer segmentation in the cloud era: A Q–R hybrid methodology and practices," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 78-92.
  • Handle: RePEc:eee:tefoso:v:91:y:2015:i:c:p:78-92
    DOI: 10.1016/j.techfore.2014.01.011
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    References listed on IDEAS

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    2. Esteban-Bravo, Mercedes & Vidal-Sanz, Jose M. & Yildirim, Gökhan, 2015. "Historical impact of technological change on the US mass media advertising expenditure," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 306-316.
    3. Ravneet Kaur & Rajesh Singh & Anita Gehlot & Neeraj Priyadarshi & Bhekisipho Twala, 2022. "Marketing Strategies 4.0: Recent Trends and Technologies in Marketing," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
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    7. Willems, Kim & Smolders, Annelien & Brengman, Malaika & Luyten, Kris & Schöning, Johannes, 2017. "The path-to-purchase is paved with digital opportunities: An inventory of shopper-oriented retail technologies," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 228-242.
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    9. Basa-Martinez, Diana Denise F. & Cabrera, Janet Y. & Dionaldo, LA G. & Orillo, Jonathan Gavino R. & Ramos, Paul John M. & Ocampo, Lanndon A., 2018. "An exploration of a respondent pre-qualifying framework to increase response rates in social media initiated online surveys," Australasian marketing journal, Elsevier, vol. 26(3), pages 239-261.
    10. Chang, Ruidong & Cao, Yuan & Lu, Yujie & Shabunko, Veronika, 2019. "Should BIPV technologies be empowered by innovation policy mix to facilitate energy transitions? - Revealing stakeholders' different perspectives using Q methodology," Energy Policy, Elsevier, vol. 129(C), pages 307-318.
    11. Manuela Rozalia Gabor & Nicoleta Cristache, 2021. "Q or R Factor Analysis for Subjectiveness Measurement in Consumer Behavior? A Study Case on Durable Goods Buying Behavior in Romania," Mathematics, MDPI, vol. 9(10), pages 1-24, May.
    12. Francesco Castagna & Piera Centobelli & Roberto Cerchione & Emilio Esposito & Eugenio Oropallo & Renato Passaro, 2020. "Customer Knowledge Management in SMEs Facing Digital Transformation," Sustainability, MDPI, vol. 12(9), pages 1-16, May.

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