Choice of Research Design

The research design is a strategy for collecting the desired information, and for answering the research question. The choice of a research design is critical for a reliable, valid and successful study. Research designs can broadly be categorized into three different approaches (Grønhaug, 1985; Selnes, 1999);

 Explorative design

 Descriptive design

 Causal design

The explorative design is applicable when there is a poorly defined research problem, and the dimensions and relations are unclear. The descriptive design is applicable in research problems where there is a well-structured research question, but the aim is not to

determine a causal effect. Finally, the causal design is applicable where the research questions includes a causal effect between to variables (Grønhaug, 1985), i.e. X causes Y.

The aim of this study is to find whether bundling will cause a higher evaluation of a new

experiments, treatments of the independent variables are utilised in order to find an effect on the dependent variable. For a causal design it is desirable to maintain the conditions for causality, including association, temporal precedence and no plausible alternative explanations (Cook & Campbell, 1979).

3.1.1. The Requirements for Causality

In research one can never be certain that there is a causal relationship, but researchers are talking about a probabilistic causal relationship between variables (Selnes, 1999). One might find relationships between test scores even though they are not of a causal character. In order to conclude with a causal relationship between variables, three requirements must be met (Gipsrud, Olsson, & Silkost, 2008; Selnes, 1999):

1. Non-spuriousness (isolation)

2. There must be a covariance between X and Y (association) 3. X must occur before Y in time (temporal precedence)

The requirement of isolation/non-spuriousness (1) implies that there cannot exist any alternative explanations for the statistical effect found in a study between two variables (Frankfort-Nachmias & Nachimias, 2008). This requirement is also known as internal validity (Edmonds & Kennedy, 2013; Selnes, 1999). In this study, the isolation requirement was fulfilled by employing an experiment on undergraduate students of economics and leadership at Buskerud and Vestfold University College, campus

Kongsberg. It is common to consider students as a homogeneous group (Peterson, 2001), which suggests disturbing factors might influence them similarly. Hence, it reduced the need for control variables. Secondly, the experiment took place in a lecture, which was considered as a laboratory under controlled settings. Experiments are the best design for fulfilling the isolation requirement, as one are quite certain that the treatment cause the effect. The treatments were controlled up against a control group and the environmental influence were controlled (Selnes, 1999). Thirdly, control variables were included in the study. To increase the internal validity in surveys, measurements of other plausible explanations can be included to exclude their effect on the dependent variable, and

therefore reject the alternative explanation. Finally, the experiment also included randomisation of treatments. Random sampling is an example of an action that can be applied for a better isolation (Bollen, 1989).

The requirement for association (2) implies that two variables have to correlate, i.e.

different levels of X have the corresponding different levels of Y (Selnes, 1999). When the cause is present, the effect should be present, and when the cause is absent, the effects should be absent (Bollen, 1989). To fulfil this requirement there has to be present a statistical significant correlation between the variables, and this correlation cannot be caused by some coincident (Selnes, 1999). In this study, the requirement of association was satisfied by analysing the difference between a treatment group and a control group, and by analysing the difference within the group. There is a chance manipulations and measures are not working as intended, and resulting in no effects found (Selnes, 1999).

Yet, lack of significant results in an experiment does not mean that the hypotheses are wrong, since measurement errors might be a disturbance to the results. The chance of measurement errors were in this study minimised by using multiple indicators for the concepts, and a 7-point Likert scale that capture the nuances in the concepts (Berry &

Feldman, 1985).

One cannot talk about a causal relationship without knowing the casual direction. The requirement of temporal precedence (3) implies that the cause must happen before the effect in time, i.e. X has to occur before Y (Cook & Campbell, 1979; Selnes, 1999). In this study, the effect was observed after the manipulations were given. However, in some situations, there might be a time lag between the manipulation and the effect, and hence the researcher cannot find an effect (Cook & Campbell, 1979; Selnes, 1999).

3.1.2. Experimental Design

The term experiment means “test” (Cook & Campbell, 1979), i.e. a test of effects of a

 Control of other conditions that might explain the cause and keep external forces outside

 Compare effects of different treatments and interactions

Experiments as a research design are categorised into true- and quasi-experiments. A true-experiment is based on a random assignment of subjects to the experimental groups, while a quasi-experiment does not involve random assignment (Mitchell & Jolley, 2013).

Based on the causality requirements discussed in the previous subsection, true experiment was the most appropriate research method for this study. This type of experiment allows for randomisation of treatments, which increase the isolation.

In addition, experiments distinguish between field- and laboratory experiments. Field experiments are performed in the environment where the phenomenon naturally occur, while laboratory experiments are conducted in a closed room with a designed situation with the phenomenon. This study involved a true experiment in a laboratory. A true experiment is the most powerful design for finding causal relationship, and it allows a high degree of control of the surroundings in order to minimise the external influence on the phenomenon (Gipsrud et al., 2008).

This study employed a between-subjects factorial approach, as there were enough participants available and the approach allows for random assignment of different treatments and comparison of the treatments (Edmonds & Kennedy, 2013). The factorial design is used when more than one independent variable are analysed in one single experiment, and they allow testing of how two treatments interact, as well as testing the main effects (Cook & Campbell, 1979; Edmonds & Kennedy, 2013; Mitchell & Jolley, 2013). In this study, the interaction refers to how brand equity of the additional product and perceived risk of the new product interacts. Due to limited time and the scope of this master thesis, the effects were only tested after the treatment was given.

To summarise, the experiment in this study is a 3 (no bundle/low brand equity

bundle/high brand equity bundle) x 2 (low/high perceived risk) between-subjects factorial posttest design. The chosen design adds to a total of six groups (see Tabel 3.1).

Table 3.1: Experimental design

Experimental groups No bundle Low Brand Equity Bundle

High Brand Equity Bundle

Low perceived risk 1 3 5

High perceived risk 2 4 6