Mobile Applications (Apps) in Advertising: A Grounded Theory of Effective Uses and Practices
The emergence of e-commerce in 1990 onwards has dramatically changed the way companies conduct business and people communicate (Peters et al. 2007; Lu and Su 2009). The 21st has witnessed a tremendous growth of mobile networks and devices, which have turn
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developed based upon the literature and in line with the research objectives covering such topics as: apps and marketing/advertising; apps and brand loyalty; m-commerce and customer behavior; and the future of apps. All interviews were audio-recorded and transcribed verbatim. Qualitative software NVivo 7.0 was used for the coding. The sample consisted of sixteen males and four females with their ages to vary from 25 – 34 to 45 – 54 years old. The demographic characteristics of the participants are shown in Table 1. All the participants worked for leading app developers in London, UK. RESULTS At the beginning of the analysis open coding was used, with the aim of identifying the initial concepts by interpreting transcripts line by line and giving meaning to the data. In total 275 open codes were identified. Some examples of open coding are illustrated in Table 2. Memos were used during data collection and analysis with the aim of identifying associations. An example of a memo is shown below: In general, privacy and security concerns do not prevent customers in accepting Ads through Apps because people are not aware of that. They just click NEXT without reading all those information when they download an App. So, customers' lack of information awareness is very important. After open coding, the core category App branding emerged through saturation and relevance that allowed us to go on to selective coding, which meant limiting coding to those variables that were related to the core category. At this stage, we tried to make sense of the chaos of 275 open codes by creating more abstract categories. In total, 65 emerging categories have been created such as, features of different mobile platforms, images of both managers and consumers and motives for positive/negative adoption of advertising through Apps. The last step, known as theoretical coding was followed with an examination of how codes may relate to each other as hypotheses to be integrated into the theory. In Figure 1, we illustrate the first model developed from the data. This model shows the market strategy of Apps, which answers the first research question. According to this model, the App market strategy is consisted of four possible App categories (Game Apps, Entertainment Apps, Utility Apps and Failure Apps) based on how low or high the longevity and frequency of use of Apps is. Game Apps: are characterized by high frequency of use and low longevity. According to managers, game apps are not an effective place for advertising. For example a manager said: “…games are very hard to advertise in, because people are generally tied into their game, they want to play and they do not reply to ads”. Entertainment Apps: are characterized by high frequency of use and high longevity (e.g. Social Media). This category is the most effective place for App-advertising and especially for non well-known brands as it offers a strong content. Particularly, a manager said that “…ultimately we think that the association with our X brand around content is the value that we can use
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