Requirements for the Implementation Methodology

The results of the study presented in this thesis propose a clear set of features, which are specific to the advertising field. For instance, they recommend linked and categorised advertisements (business owner question in section 4.2.2 and Appendix B, the comments of business owners in section 4.3.2 and open comments in section 4.3.1, and further detailed and discussed in Chapter 6, section 6.2),

adaptation rules (hypothesis H2, as defined in section 4.2, and further explored and discussed in Chapter 7, section 7.2), user characteristics/behaviour (hypotheses H1 and H3, defined in section

62 4.2, and further explored and discussed in Chapter 8, section 8.2) and location on the webpage

(further discussed in Chapter 9, section 9.3). These are all necessary to identify the most suitable advertisements for each online user, and are not available and supported by previous existing models. Thus, a model that supports adaptive advertising has to implement all these features (see Chapter 5).

The requirements for the implementation methodology are identified, based on the outcomes of the exploratory case studies above and the literature review, as follows.

1. The methodology must prioritise flexibility and agility, thus prototyping should be applied to generate the AEADS system. A first version of the system will be generated, validated, tested, and finally evaluated (Chapters 6-9). The evaluation analysis data will then be used to generate the second version of the AEADS system (Chapter 9).

2. The methodology will rely on social networks as the primary source for extracting attributes of the user model form (Chapter 8).

3. The methodology permits the author to categorise the advertisements in the domain model (Chapter 6).

4. The methodology should enhance the user model structure, by dividing it into multiple levels. This will enhance the processes for storing and retrieving data (Chapter 8).

5. The methodology should introduce an easy graphical user interface tool, to easily create the adaptation rules (Chapter 9).

6. The methodology should ensure that the system monitors the use of advertisements (Chapters 8, 9).

 Find out how many people click on each advertisement.

 Find out how many people view an advertisement and do not click.

7. The methodology proposes to add a social interaction element to advertisements, such as ‘like’ or ‘stop’ options (see Chapter 9).

The proposed system for personalised advertisements is heavily based on e-learning adaptive systems, and that the application of similar systems in advertising is in its infancy.

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4.6.Conclusion

In this Chapter, an exploratory study has been presented, in relation to the challenges and primary elements of the overall study. The user-centred design method has been utilised in this study, according to the ISO-standard 13407, so that the preferences of Internet users and business owners could be examined. Overall, the primary result of this Chapter, along with the outcome of the research, has shown that businesses would prefer to send out personalised or segmented advertising messages. Based on the research results, a new theoretical model and adaptive e-advertising system have been proposed, to enhance the organisation and adaptation of advertisements on any website it is introduced to. The purpose for this methodology is to create a generalised system that can integrate and work with any website. Moreover, the importance of social networks as the primary sites for extracting users’ characteristics has been brought to the fore and discussed. To this end, social networks have an increasingly important part to play in identifying users’ attributes. An evaluation methodology has been proposed for each layer of the system.

Furthermore, Internet advertising is a growing revenue stream that many businesses are considering. However, personalisation may be the key to ensuring effectiveness of the advertising. Social network analysis is a growing area of knowledge and, as shown in this instance, it is also an effective source of complex user data that have the potential to revolutionise e-advertising.

In summary, the research shown in this Chapter has implemented (the implemented parts are underlined) the research objective O2: “Design a set of preliminary studies with businesses and users, to establish the current state of art in the area of adaptive advertising and to gather the requirements for the design and implementation of an appropriate theoretical model and system”.

The procedure for analysing this objective has been outlined and the outcomes have helped to answer the research questions R1: “Is adaptive advertising useful for businesses and users?”, R1.1: “Is it more acceptable for users to have adverts personalised to them and their environment? (i.e., do users find personalised adverts more acceptable than non-personalised)”, R1.2: “Is it more acceptable for businesses to deliver adaptive advertising? (e.g., do business users find adaptive advertising more acceptable when compared to non-adaptive advertising, and do they expect the former to provide a

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better income)”, and R1.3: “What is a good source of information for adaptive advertising?”. The

answers to these research questions are presented in this Chapter and were produced through gathering the requirements of business owners and Internet users. In addition, they will be answered through designing an appropriate model and system for adaptive advertising. The details of the model and system are discussed in Chapters 5-9, where the research questions R2 and R3 are answered.

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Chapter 5