Erasmus Universiteit Rotterdam Willingness to pay for mobile apps
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- 3.4 Measuring control variables
3.3 Measuring App attributesFor this study we use the full profile method in which all used attributes of the App are presented to the respondent. A benefit of the full profile method is the ability to measure overall preference judgments directly, using behaviorally oriented constructs like intention to buy, likelihood of trail and so on (Green and Srinivasan, 1990). Determination of the relevant attributes and the relevant levels of each attribute (table 3) resulted in a 52, 22, 31 attribute space with 300 possible combinations. Price is included in the orthogonal design because it will probably have an effect in its own right for on the customer in making his choice. Another argument is that developers do not base their prices on the level of attributes that are subject to this study, but based on their own perceptions of the value of the app or pricing strategy (the price could also be seen as quality indicator). We created stimuli and conjoint choice sets according to a computer-generated orthogonal design that accounted for the design principles of minimal overlap, level balance, and orthogonality (Huber and Zwerina, 1996). To administer the study, we used SPSS Software. “Generate Orthogonal Design” in SPSS generates a data file containing an orthogonal main-effects design that permits the statistical testing of several factors without testing every combination of factor levels. The minimal number of cards for the 300 possible combinations was 25. Appendix B outlines all the 25 choice cards generated by the orthogonal design procedure in SPSS. The respondent will be presented with 13 choices between 2 choice cards (i.e. conjoint stimuli) so that all the cards are evaluated. The respondents are told to imagine that they need to choose among the product alternatives in an Application Store “right here” and “right now.” 3.4 Measuring control variablesAs discussed in the previous chapter, individual properties like m-commerce involvement and a person’s payment method could contribute to different preference structures of the displayed attributes in the Application Stores. To know if these personal properties have effect on a person’s preference structure, we added a couple of questions in the 3rd part of the survey to measure control variables. In table 4 and table 6 you can find the constructs for the control variables, involvement and payment method. For this study we are also interested in the potential difference between the types of Apps. To investigate this we included the 4 categories mentioned in paragraph 2.8. In table 5 you can find these categories together with the chosen Apps that will be presented in the choice sets of the respondents. For an appropriate analysis of the type of apps, every type should have a conjoint design that accounts for the design principles of minimal overlap, level balance, and orthogonality. Unfortunately, due to limited time and resources, this is not possible in this research.
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