It appears that causal-comparative research designs are not common in Information System science. Would like to learn if my observation is accurate.
A causal-comparative design is a research design that seeks to find relationships between independent and dependent variables after an action or event has already occurred. It seeks to establish a cause-effect relationship between two or more variables. The researcher does not assign groups and does not manipulate the independent variable.
The researcher's goal is to determine whether the independent variable affected the outcome, or dependent variable, by comparing two or more groups of individuals. It typically starts with the effect and seeks to find possible causes and is used when studying differences where the variables cannot be manipulated.
Types of Causal-Comparative Research:
1) Retrospective Causal-Comparative Research – In this type of comparative research, the researcher is investigating a research question after the effects have already occurred. The researcher studies how one variable may have influenced another variable. This is the most common type of causal-comparative research,
2). Prospective Causal-Comparative Research - Researchers using this type of method begin by studying the causes and then investigating the possible effects.
Causal-comparative research designs are mostly used in education, social sciences, etc. but may not not be used in information systems science because of a different subject matter and technical problems.
A causal-comparative design is a research design that seeks to find relationships between independent and dependent variables after an action or event has already occurred. It seeks to establish a cause-effect relationship between two or more variables. The researcher does not assign groups and does not manipulate the independent variable.
The researcher's goal is to determine whether the independent variable affected the outcome, or dependent variable, by comparing two or more groups of individuals. It typically starts with the effect and seeks to find possible causes and is used when studying differences where the variables cannot be manipulated.
Types of Causal-Comparative Research:
1) Retrospective Causal-Comparative Research – In this type of comparative research, the researcher is investigating a research question after the effects have already occurred. The researcher studies how one variable may have influenced another variable. This is the most common type of causal-comparative research,
2). Prospective Causal-Comparative Research - Researchers using this type of method begin by studying the causes and then investigating the possible effects.
Causal-comparative research designs are mostly used in education, social sciences, etc. but may not not be used in information systems science because of a different subject matter and technical problems.
The information system is not incompatible with scientific cause-effect research, nor with comparative analysis.Sometimes performing comparative studies may be facilitated by the use of elements in the computer system, but this can not be generalized, as the exception.
I see no reason why laboratory and field experiments cannot be used in information systems science as a method for the behavioural investigation of causal relationships. With both methods, an experimental variable can be manipulated in a repeatable manner within a controlled environment and the effect of the manipulation can be measured. The object of investigation can be examined either in its natural environment ("field") or in an artificial environment ("laboratory"). However, I am not aware of any current figures for German business informatics. There are only the results of a study from the year 2007, which tend to prove you right. Laboratory experiments were only carried out in 9% of the cases. An English summary of the results of this study can be found here: https://link.springer.com/content/pdf/10.1007%2Fs11576-007-0064-z.pdf This article also discusses the different research designs, so it might be helpful for you.
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