Select/Build the Case

Selecting the appropriate case is a challenging step especially with the lack of consensus on what a case is (Gerring, 2004; Ragin & Becker, 1992), or in Yin’s (2014) term what the research unit of analysis is.

The case selected should reflect the research problem and objectives since cases are selected based on their relevance not merely because they are interesting (Eisenhardt, 1989; George & Bennett, 2005). Given the focus on examining security networks, the chosen case should reflect the notion of networks; it should provide the opportunity for studying interactions between heterogeneous actors. In other words, the case ought to be prevention encounters rich. If prevention encounters are not observable in the proposed case, considering an alternative one would seem inevitable to be able to answer the research question.

Taking these issues into consideration, the case of credit card fraud was selected to examine and generate a process model on how security networks achieve prevention. Specifically, three reasons derived the selection of this case. First is theoretical relevance. The heterogeneity of actors involved in the credit card industry (technology, banks, regulatory agencies, merchants, and customers) and the complexity of their relationships (Lablebici, 2012) make the case ‘prevention encounters rich’, which is the heart of this research. Second is practical significance. Statistics show that the financial sector is among the top sectors exposed to security threats (Choo, 2011; Symantec, 2009). The total credit card fraud losses in the U.S. was approximately $7.5 billion in 2015 and this is expected to increase (Statista, 2016). Furthermore, the number of credit card usage in offline and online transactions is in continuous growth (Capgemini & RBS, 2013) making credit cards an indispensable technology in our daily lives. Third is future implication. Credit cards are considered the technology that ignited electronic value exchange (Naar & Stein, 1975), by understanding its case we can draw further implications on collective security efforts and incentive mechanisms necessary to face security threats arising from continuous innovations in digital payments.

instance, in 2012 the U.S. accounted for 47.3% of the worldwide card fraud losses (PCM, 2015). A second reason is the richness of data sources available for preventing credit card fraud in the U.S.

Credit card fraud represents the general case which then went through casing process (Ragin, 1992) to generate embedded cases within the general one (Yin, 2014). Those embedded cases (or sub-cases) are exemplified by the research construct ‘prevention encounters’, with each prevention encounter signifying an embedded case. This theorizing of the research case enables both within-case and cross-case analysis improving the reliability of the generated theory (Eisenhardt, 1989). Cases therefore are not merely selected but also inductively built or constructed by the researcher. They become flexible and manipulable to allow a particular focus that guides empirical work while at the same time be shaped by both theory and empirical evidence (Wieviorka, 1992). An important aspect of casing then is delineating the case so that one will be able to locate them within the voluminous research data (Yin, 2014; Ragin, 1992). When confronting a piece of information, we need to be able to judge whether it falls within the scope of the research or not, if it is part of the case or external to it, i.e. we need a mechanism that specifies how to cut the general case into embedded cases. In investigating the case of credit card fraud, I am interested in collective efforts pursued to prevent the phenomenon; specifically, I am interested in prevention encounters, so my case or unit of analysis is prevention encounters. But since this is a construct developed by the researcher, a mechanism had to be established to specify how cases around that construct are built; that is we need to know the exclusion and inclusion criteria that set the boundaries of the sub- case.

One way to draw the case boundaries is available literature, which has already been visited during the first step when identifying the research problem and objectives. While reviewing the literature, researchers might develop conceptual frameworks with constructs of interest. These frameworks are of great value since the same case can be seen from different angles and theoretical lens. Frameworks remind researchers with the purpose of their research and accordingly what the study should be a ‘case of’. Though having predefined constructs may sound contradicting given the emphasis on theory generation, initial constructs or frameworks helps in

designing a well-defined focused research. They serve as building blocks for determining what the case is and is not about (Miles et al., 2014). The aim is thus narrowing down what constitute a case leaving analysis and relationships between these constructs (along with others identified while analyzing the data) to a later stage (Andersen & Kragh, 2010; Eisenhardt, 1989).

Accordingly, to identify the prevention encounters and their boundaries I relied on my conceptualizing of the construct. Prevention encounters represent actions taken by heterogeneous actors to develop and adopt prevention measures that shake an established pattern. They are triggered as a response to certain events that constitute a turning key point in security practices. According to this conceptualization, casing from credit card fraud is done according to the following three main criteria:

1. Actors’ actions have to be related to developing and adopting prevention measures.

2. Actions have to be initiated by prevention encounters triggers (social pressure, regulations, and technology).

3. Actions are only seen as prevention encounters if they shake an established security practice.

The above protocol was used in the casing process and resulted in creating eight prevention encounters.