Digital Addiction, (hereafter referred to as DA), has become a serious issue that has a diversity of socio-economic side effects. In spite of its high importance, DA has received little recognition and little guidance as to how software engineering should take it into account. This is in stark contrast to other domains known for traditional addiction, e.g. drugs, gambling, and alcohol, in which there are clear rules and policies on how to manufacture, market and sell the products. We contend that software engineering is required to attempt to provide ways to develop software that does not lead to addiction and to accommodate users who are genuinely vulnerable to addictive behaviour. This thesis will concentrate on conceptualising DA to advance the understanding of how addiction elements influencing features and functionalities of social networking systems.
Digital Addiction (DA) can be described as a significant degree of dependent behaviour
that is triggered and facilitated by software products. It can lead to both pleasure and relief
of discomfort, but unfortunately, in a way that can harm a person socially, physically and
psychologically. However, despite its impact on society, DA is still considered outside the
boundary of the software engineering. That is, unlike the situation with drugs or alcohol,
software engineering has, so far, not been charged with the responsibility for dealing with
or mitigating the effects of DA. DA raises new challenges to software engineering in
general and requirements engineering in particular. Our research argues for novel
approaches, which are able to cater for the diversity, subjectivity and also the private
nature of information related to DA, i.e. users’ “soft issues” [
The number of publications addressing the role of software design in DA is very
limited, e.g. [
The aim of this research is to conceptualise “digital addiction” and investigating adaptation-centred user requirements to support social software development.
Game addiction will be out of our focus due to its special attributes related to
attractiveness features, e.g. visualisation, flow experience, competitions, flexibility
characteristics and rewarding mechanisms [
The study will target social networking applications, e.g. Facebook, as they have
become the dominant theme on the Internet. We have adopted a criterion to determine
what we mean by social networking applications. First, it is these applications within a
virtual space and have the following special features: persistence, searchability,
replicability and invisible audiences [
Our main objective focused on conceptualising DA to advance the understanding of how addiction elements influencing features and functionalities of social software systems. We hope that findings enable us to build requirements engineering framework for DA and to be supported by guidance materials to help software engineers derive DA requirements.
As communicating these requirements through the development of social software requires an approach; we would provide some guidelines supported by examples to demonstrate how that can be achieved. This work would also provide insights on how RE adapt to cope with DA.
To make this objective manageable and easier to achieve, we have broken it down into intermediate and initial objectives and activities. These are summarised in Figure 1.
In this section we present the method and high-level plan for the research. While, exploratory study is always broad and not fit to answer specific questions, the purpose is to conclude the adequate research design, data collection, subjects selection criterion and to test the feasibility of all of that. Ultimately we hope that studying User Experience (UX) could, ideally, provide insights on the “why” questions. Several studies, e.g., [1316], showed that user experience is not negatively affected even when social software such as YouTube, Facebook, Wikipedia have poor compliance to usability principles. Therefore, to understand the true nature of DA, the broader scope of UX need to be incorporated by including “felt experience” such as “pleasure, curiosity, and selfexpression”, and also what users gain, rightly or wrongly, from particular behaviours. Law et al. [17] concluded that UX is “dynamic, context-dependent, subjective” and must be grounded in User-Centred Design (UCD) practices. For this reason and due to the lack of studies that attempted to associate software aspects to DA, the research will start with using grounded theory approach (i.e. qualitative research).
nd t?p a a y d h a tw to ,a n hW ehw
Content Analysis
Diary Study Observational Experiment
Interviews Ontology
Awareness Requirements Adaptation Mechanisms Trade-offs
Guidelines Questionnaires
Expert Survey Literature Review Applications of Use Recommendations Lessons Learned
Conclusions
Problem space Problem scope Research Questions
DA Principles
Hypotheses A literature review will be first done to consolidate our understanding of DA and its relation with software design. Given the fact that each method has its own distinct concerns and limitation, it is more ideal to incorporate more than one method to gain better understanding of the phenomena. Therefore we would apply the following methods:
We will be considering four-dimensional factors to give sense of scope for the phenomenon, these are; (1) Social software in terms of software design, requirements and goals for which this software is being used; (2) Context of use; (3) Users’ personal characteristics; (4) Interaction between the social software and users. The selected sources will be analysed based on the following questions: What factors contribute to DA? What are key concepts/dynamics around the DA? What personality traits make users more susceptible to DA?
Diary Studies are a type of remote testing and contextual inquiry method used in HCI research to capture data from users in their own settings [18]. This method can enable capturing the causes, i.e. external triggers, of certain emotions such as impulsiveness and how users responded to such feelings. We can also utilise time diaries to find out how much time spent due to a particular feeling.
Users’ behaviours will be observed during controlled experiment on selected social software to enable capturing patterns of use, classifying users and identifying software addictive aspects. This will involve collecting some quantitative and qualitative data. Users’ thoughts and emotions will be the basis of the qualitative data to identify motivations and preferences. Quantitative data will include some statistical data about users’ interaction with the software features and also their emotional response data by some UX metrics [19]. Interviewing subjects will follow to enhance expected results.
The inherent diversity of users and the complex nature of DA require collaborative efforts. Therefore, the outputs of objectives 1 and 2 will be used to conceptualise DA components in a form of ontology, to facilitate knowledge sharing, communication and collaboration.
We will investigate how to exploit collaborative ontologies to elicit users perceptions towards DA with the aid of annotation/tagging capabilities to allow collaborators to interact with the model and feed it with community-driven semantics. This with the aid of some techniques such as Wiki-driven ontology [20], Folksonomy [21] and FolksOntology [22]. 4.4
Adaptation decisions require identifying what to observe in the environment and the
systems itself [23]. The output from the previous objectives should enable identifying
what to observe, the features and factors that can be adapted, when, how and, probably,
where they can be adapted. This adaptation model is looking to adaptivity features and
filtering options, such as persona-based filtering options, attribute-based filtering options
and extra filtering to do with other extracts of the DA modelling. This will provide
software engineers with sort of requirements to support adaptation decisions. This might
include HCI requirements, i.e. heuristics, software development requirements and
requirements to do with users’ motivations, values and emotions [
I am currently finalising the ontology I developed for the concept of DA. I have also produced logical models to identify the main components of DA in different definitions found in the literature to enhance our understanding of the concept. 12.Kietzmann, J.H., Hermkens, K., McCarthy, I.P., Silvestre, B.S.: Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons. 54, 241–251 (2011). 13.Hart, J., Ridley, C., Taher, F., Sas, C., Dix, A.: Exploring the facebook experience: a new approach to usability. 471–474 (2008). 14.McCarthy, J., Wright, P.: Technology as experience. interactions. 11, 42–43 (2004). 15.Silva, P.A., Dix, A.: Usability: not as we know it! 103–106 (2007). 16.Thompson, A.-J., Kemp, E.A.: Web 2.0: extending the framework for heuristic evaluation. 29–36 (2009). 17.Law, E.L.-C., Roto, V., Hassenzahl, M., Vermeeren, A.P., Kort, J.: Understanding, scoping and defining user experience: a survey approach. 719–728 (2009). 18.Lazar, J., Feng, J.H., Hochheiser, H.: Research methods in human-computer interaction. (2010). 19.Agarwal, A., Meyer, A.: Beyond usability: evaluating emotional response as an integral part of the user experience. 2919–2930 (2009). 20.Hepp, M., Bachlechner, D., Siorpaes, K.: OntoWiki: community-driven ontology engineering and ontology usage based on Wikis. Presented at the WikiSym '06: Proceedings of the 2006 international symposium on Wikis August (2006). 21.Vander Wal, T.: Folksonomy, http://vanderwal.net/folksonomy.html. 22.Van Damme, C., Hepp, M., Siorpaes, K.: Folksontology: An integrated approach for turning folksonomies into ontologies. Bridging the Gap between Semantic Web and Web. 2, 57–70 (2007). 23.De Lemos, Rogério, et al.: Software engineering for self-adaptive systems: A second research roadmap. Software Engineering for Self-Adaptive Systems II. Springer Berlin Heidelberg, 1-32 (2013).