Personalized Advertising Project Progress -- Week 2

This week I do some literature review for the project and have mainly studied these three pieces: Personalized mobile advertising: Its key attributes, trends, and social impact (Chen & Hsieh 2012), The key role of relevance in personalized advertisement (Zhu & Chang 2016) and The SAGE Handbook of Qualitative Data Analysis Netnographic Analysis : Understanding Culture Through Social Media Data (Kozinets, Dolbec & Earley 2013). I learnt a lot about personalized advertising (PA) and its relevant attributes as well as how to measure the value of different attributes from the former two pieces. And the third paper offers another way to research social media-related issues that is Netnography.

Chen and Hsieh (2012) list dozens of attributes of PA on mobile devices which are concluded from previous literature and are categorized into three dimensions: context, content and personal profiles. The researchers applied fuzzy Delphi expert questionnaires to evaluate which attributes among them are indeed valuable as to the effect of PAs. After conducting survey among experts in the industry value chain, they use Delphi method to define the “consensus importance value” of each attribute and thus come to the conclusion that device, promotion, price, brand, preference and interest are the most important attributes to make a PA successful. Based on their research outcome, I could come up with my own hypothesis of important attributes and use similar analyzing method to determine whether they are. But in my research, I would mainly cover the stakeholder which is missing in this research--customer. Advertising and media experts thought certain elements of PA would be accepted and welcomed, but do customers agree? I believe my research is gonna complete the puzzle and offer more references to the decision makers in the PA industry.

Zhu and Zhang (2016) studied on the relationship between customers' privacy concern and the relevance of PA. The research applied structural equation modeling to test the correlation between participants' responses towards different concepts. They concluded that the relevance of PA to audience would alleviate their privacy concern and promote their continuous use of the applications. Relevance indeed is an outstanding feature of PA and I would also examine the role it plays in customers' mind.

Kozinets, Dolbec and Earley's introduction on Netnograophy inspires me that aside from the survey, I can also utilize social media themselves to see what customers say or react to PA. For example, on Facebook, I can collect the data of audience's comments below PAs, or compare the received likes of different PAs to see what are popular PAs like.

Based on the literature review I have done so far, I would choose time, promotion, price, brand and relevance of PA as my main focus and I haven't decided on how to analyze data: to use Delphi method to define the value of each attribute, or use correlation test to find out which attributes affect customers' attitudes. Also, as a supplement of the survey, for the coming weeks I need to read more about how observation works.

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