Information systems are developed to improve the efficiency within an environment or an organization. Such creations are often of a complex nature and can be studied at several levels, such as knowledge about the development of applications, as well as information technology at a managerial level [26]. One might therefore argue that two different, but complementary, research paradigms are needed to grasp the complexity of information technology: behavioural science and design science [26].
Behavioural science has roots in natural science research methods and is about explaining how and why things are the way they are. The end goal of the research paradigm is truth [26] [27]. Behavioral science is revolving around developing and justifyingtheories, where progress is achieved when the theories provide more accurate explanations of phenomenons then past ones, and success can be measured by the theories predictive ability of future observations [27] [26]. In an information technology context this can result in theories related to a system’s usage, usefulness, and impact within an organization [26].
Design science, on the other hand, has roots in the engineering field and is a problem-solving paradigm that seeks to build innovative artifacts by applying knowledge of tasks and situations to the building process [27]. Design can in other word be regarded as both a process and an artifact where the goal is utility. In the end knowledge and understanding of the problem domain are achieved through the development and usage
of the designed artifact [26].
Novelty While design science is based on creating artifacts, it should not be mixed with system development as a routine design. The latter is about applying existing knowledge to solve organizational problems using best practices. Design science is con-trarily addressing unsolved problems in an innovate way, or solved problems in a more efficient way. Furthermore, design science research has a clearly identifiable contribution to a knowledge base [26].
It is difficult to build something really new, as most work is based on previously existing ideas or products. Innovation might, however, take several different shapes as seen in figure 2.1, such as improvement by implementing new solutions to existing problems, exaptation by extending known solutions to new problems, and invention with new solutions to new problems [1].
Figure 2.1: Contribution matrix [1]
Process
Similar to the development and justification of theories in behavioural science the design sciences process is mainly built on two stages, building and evaluating [27]. This loop between building and evaluating is usually performed several times before the final artifact is complete [26].
Building
Building artifacts as a part of design science research is a pursuit of an artifact with a specific purpose, proving in the process that such an artifact can be developed [27].
The end products of design science are generally described as either Constructs
Constructs assist the composition of vocabularies, enabled knowledge sharing within a domain. Such conceptualizations include for instance entities, attributes, and consensuses [27].
Models
Models are built upon a set of constructs and their relationships in a formal man-ner, resulting in representations of the real world such as an Entity-Relationship Model (ER-model) [27][26].
Methods
Methods are a way to perform goal-directed activities [27]. They are in other words providing guidance on how to solve problems using, for instance, mathematical algorithms, textual descriptions of approaches, or a combination [26]
Instantiations
The instantiations are a realization of an artifact in its environment, capable of solving a specific task by operationalizing constructs, models, and methods [27]. Furthermore, the implementations prove the feasibility or effectiveness of the models and methods that are included in the artifact’s implementation [27].
Evaluating
The evaluation phase is concerned with assessing the utility provided by an artifact in order to solve a given problem [26]. The evaluation results in more information and a better understanding of the problem space, highlighting the improvement potentials in terms of both the building processes and the artifact [26].
Performance is a relative term connected to the intended use since artifacts can potentially solve several different problems [27]. The evaluation metric is therefore dependent on the particular artifact’s intended environment defining what it is trying to accomplish [27]. Such metrics might be based on functionality, completeness, reliability, usability, or how good the artifact is fitted to the organization [26]. The overall progress is achieved when old technology is surpassed by more efficient innovations [27].
Knowledge base
There are two types of scientific research in the information technology practice, de-scriptive and prede-scriptive. While the behavioural science field is generally based on descriptive knowledge, design science is corresponding to prescriptive research activities [27].
Incomplete understanding of the environment where the problem is originated can result in poorly designed artifacts or unforeseeable side-effects. The creation of artifacts is thus dependent on what is calledkernel theory [27]. The kernel theory refers to any descriptive knowledge used to inform the artifact building process about the problem or its environment. This knowledge may have different forms, such as observations of a phenomenon, principles, and natural laws [1].
From the prescriptive knowledge base the researcher can in a design science study investigate similar known artifacts that have been used to solve a similar problem. This may assist the process of setting a knowledge baseline by indicating the level of novelty in the new artifact and by providing knowledge [1].
Knowledge from behavioural science and design science are accordingly both im-portant as they provide the raw materials to a design science research project. This through foundations from historical research on either information systems or referenced disciplines, and methodologies providing guidelines used to justify theories and evaluate artifacts [26].