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3.1 Design Science Research

The IS research discipline is characterized by two major research paradigms: behavioral science and design science (Hevner et al. 2004). While behavioral science aims to develop and evaluate theories regarding the behavior of humans and organizations, design science aims to develop new and innovative artifacts (Hevner et al. 2004; March and Smith 1995; Winter 2008). The research work at hand predominantly follows the design science research paradigm (Peffers et al. 2007) for structuring the research process.

Several different research results have been developed in this work in order to address research questions RQ1–RQ4. Research results that follow the design science research paradigm can be classified within the research output schema of March and Smith (1995), who develop a classification of relevant design science artifacts. These research outputs are constructs, models, methods, and instantiations (March and Smith 1995). Constructs form a specialized language and explicate the shared knowledge of a particular domain, such as the mentioned modeling techniques for business processes (Keller et al. 1992; Object Management Group 2005; Object Management Group 2006) or data warehouses (Becker et al. 2007d; Bulos 1996; Golfarelli et al. 1998; Sapia et al. 1998). “A model is a set of propositions or statements expressing relationships among constructs” (March and Smith 1995, p. 256). Applying BPMN to develop a concrete business process model is one example for a model artifact. A method comprises a set of steps that are used to perform a special task. Methods make use of constructs and models and represent tasks for working with these artifacts, such as transforming models from one representation to another one. Finally, instantiations realize and implement one or more artifact(s) either as a specific tool or as an information system (March and Smith 1995).

Based on design research and research about design research, Peffers et al. (2007) developed a process model that guides rigorous and relevant design science research. The design science research methodology (DSRM) consists of six partly iterative phases, which are depicted in Fig. 3.1. In the following, the main tasks of the six phases and its inputs and outputs are briefly explained in accordance to Peffers et al. (2007).

Fig. 3.1
figure 1

Design science research methodology. Adapted from Peffers et al. (2007)

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  • Problem identification and motivation: The first phase in the design science process aims to identify and conceptualize the problem and evaluates the value of a solution. By conceptually atomizing the problem, a possible solution loses complexity and the problem becomes manageable. Expressing the value of a solution to the problem at hand motivates researchers to work on a problem solution. As a result, inferences for a possible solution are derived.

  • Define the objectives of a solution: Based on the identified problem and research motivation, the second phase aims at defining the objectives of a solution, which might be quantitative (e.g., the performance of the algorithm should be increased by 10 %) or qualitative (e.g., the usability of the interface must be improved). Fulfilling this phase requires fundamental knowledge of the current state of the problem, the current state of the solution, and the efficiency of the solution. The results of this phase are theory-guided objectives, which can be applied for the solution development.

  • Design and development: Aiming to design and develop artifacts that comprise solutions for the identified problem (constructs, models, methods, and instantiations), the third phase includes the determination of the functionality and architecture of the solution to be developed. Thereby, knowledge to solve the identified problem is generated, which acts as input for the demonstration phase.

  • Demonstration: The goal of the demonstration phase is to prove that the idea for solving the identified problem works. This is facilitated by showing that the developed solution can be used to solve one or more instances of the described problem. Several different research methods can be applied for this research step, such as case studies, simulations, or experiments. The results of this phase are metrics and analyses, which provide knowledge about the applicability of the developed solution.

  • Evaluation: Compared to the demonstration phase, the evaluation phase aims to measure how well the developed artifact works for solving the identified problem. One essential task within the evaluation is the comparison of the observed application results with the former defined objectives of a solution. The evaluation can comprise different methods to get appropriate empirical evidence or logical proof that the designed artifact is a proper solution to the identified problem. Depending on the results, the researcher may go back to the design phase in order to find a different solution to the problem.

  • Communication: Hevner et al. (2004) and Archer (1984) argue for the need to communicate research results. This last step in the design science research process comprises the continuous publication of research results in scholarly and professional journals and conferences. Therefore, knowledge about the scientific discipline and relevant outlets is necessary.

3.2 Complementary Research Methods

A research method consists of a couple of sequential operations that acquire knowledge and lead to predictable results (Iivari et al. 1998; Mingers 2001). Mingers (2001) argues for the desirability of applying a multi-method approach in IS research. Applying a pluralist methodology in IS research has two major advantages. First, the plurality of structures in the real world makes it necessary to analyze the generated events with different methods. Second, research is not seen as a single event. Rather, research work is perceived as a process containing different tasks and problems, which require different methods (Mingers 2001). Many IS researchers follow this methodology pluralism (e.g., Hevner et al. 2004; Iivari et al. 1998; Peffers et al. 2007).

Each research result presented in this book has been developed by applying one or more research methods, which are briefly described in the following. Common research methods in IS research are interviews and focus group interviews, informed arguments, literature reviews, method engineering, surveys, laboratory experiments, and case studies. They are applied and discussed in several IS research papers (e.g., Boudreau et al. 2001; Chen and Hirschheim 2004; Iivari et al. 1998; Mingers 2001; Rosemann and Vessey 2008; Vessey et al. 2002). In the following, each complementary research method, which is applied in this work, is briefly described.

  • An interview is not simply an exchange of questions and their corresponding answers. Rather, an interview is a process, in which two or more persons are actively involved and which leads to the creation of a collaborative effort to creating a contextually bound story (Fontana and Frey 2005). The interview process is controlled by the researcher and can be conducted as one-to-one or as a group interview (Oates 2006). An interview process can be structured or unstructured. Structured interviews are guided by a question guideline, whereas unstructured interviews constantly react to the interview flow without a clear question structure. Data from interviews can be collected in three ways, either by making field notes of what the interview partner says or by using audio or video tape recording, followed by a transcription (Oates 2006). A special kind of interview is the group interview, also called focus group interview (Kvale 1996; Oates 2006). Focus groups use group interaction as one major part of the method. A common group size is between 8 and 12 individuals plus a moderator, who promotes the discussion and ensures that the discussion focuses on the topic of interest (Stewart et al. 2007). Independent of the concrete interview type, the method must be used carefully because of several biases, such as socially desirable responding (Podsakoff et al. 2003), which may hinder a proper research process.

  • Informed arguments belong to the descriptive evaluation methods and make use of information from an existing knowledge base and derive convincing arguments for the usefulness of artifacts (Hevner et al. 2004). “Argument is a means of discovering truth, negotiating differences, and solving problems” (Yagelski and Miller 2012, p. 2). To create an informed argument, often patterns of logic, particularly for inductive and deductive reasoning, are applied. Hevner et al. (2004) suggest to use informed arguments as a means to evaluate IS research artifacts.

  • A literature review is the backbone of each scientific work and provides a foundation for advancing the knowledge base (vom Brocke et al. 2009; Webster and Watson 2002). Rowley and Slack (2004) define a literature review as “a summary of a subject field that supports the identification of specific research questions” (Rowley and Slack 2004, p. 31). Several procedures for conducting literature reviews have been suggested in the IS literature (e.g., Fettke 2006; vom Brocke et al. 2009; Webster and Watson 2002). They all have in common that they suggest to use a structured forward and backward search after an initial search process has been conducted. In this way, the whole body of knowledge is investigated and it can be ensured that all relevant research works for one topic are considered in the literature review. The goal of each literature review is not only to provide information about the past research. Rather it should endup in a research agenda, which can be used to close the identified research gaps (vom Brocke et al. 2009). Reviews do not necessarily focus on scholarly journals and conference proceedings. They may also be applied for analyzing newspaper articles or online reports.

  • In order to develop IS design methods, the concept of method engineering (ME) has been introduced (Brinkkemper 1996; Brinkkemper et al. 1999). “Method engineering is the engineering discipline to design, construct and adapt methods, techniques and tools for the development of information systems” (Brinkkemper 1996, p. 276). It is closely related to situational method engineering, which aims to develop project-specific methods, derived from other method fragments (Harmsen et al. 1994). One example is the adaptation or extension of a general data modeling technique, such as the ERM, to a special project context. Both method engineering and situational method engineering aim to formalize the usage of methods for system development (Henderson-Sellers and Ralyté 2010). Ralyté et al. (2004) distinguish four ME types. While ad hoc ME approaches start developing a method from scratch, paradigm-based ME approaches build upon existing models or meta-models to instantiate, abstract or adapt them to develop new To-Be models. Extension-based ME approaches extend existing methods and enhance these methods with new constructs. Assembly-based ME use method fragments in order to recreate a new method. Therefore, the concept of method components is used (Ralyté et al. 2004).

  • A survey is a means to elicit characteristics, actions, or opinions of a large group of people (Tanur 1982). According to Pinsonneault and Kraemer (1993), a survey for research purposes has three characteristics. First, the survey produces quantitative aspects of the investigated population. Since the survey method belongs to the group of quantitative research methods, standardized information about the subjects are necessary. Second, the main way of collecting data is the usage of questionnaires with standardized questions. The questions of such questionnaires might be about the respondent himself or about other analysis objects. Third, in general, data is collected about a fraction of the whole population, a so-called sample. The sample should be large enough to allow for statistically significant analyses (Pinsonneault and Kraemer 1993). Conducting a survey requires the consideration of some guidelines for preventing a bias of respondents. Several guidelines to prepare questionnaires have been published so far. The development of questionnaires in this work is guided by the work of Dillmann et al. (2009).

  • Experiments in a controlled environment are used in order to validate IS research artifacts. The so called laboratory experiments take place in a setting, whose variables and assignments of participants to various treatments are controlled by the researcher (Boudreau et al. 2001). Vessey et al. (2002) differentiate laboratory experiments in experiments with human subjects and software experiments. Whereas experiments that include human subjects focus on the observation of problem-solving situations, software experiments are characterized by the comparison of one or more systems based on various dimensions (Vessey et al. 2002). This book focuses on laboratory experiments with human subjects.

  • A case study “examines a phenomenon in its natural setting, employing multiple methods of data collection to gather information from one or a few entities (people, groups, or organizations)” (Benbasat et al. 1987, p. 370). It is conducted in the field (e.g., at a company’s headquarter, office, or branch) and can also be described as a field study (Chen and Hirschheim 2004). A case study makes use of several data collection methods, such as sighting documents, conducting interviews, physical artifacts (devices, tools, etc.) or observing the field environment (Benbasat et al. 1987; Yin 2003). Yin (2003) differentiates six types of case studies. Single and multiple case studies refer to the number of cases they observe. Both types can be combined with the primary character of the case study, which can be exploratory, descriptive, and explanatory. Case studies are applied in different disciplines, such as accounting (Scapens 1990) or operations management (McCutcheon and Meredith 1993). In IS research, case studies are one of the most applied research methods (Chen and Hirschheim 2004; Orlikowski and Baroudi 1991). According to Benbasat et al. (1987) the application of case studies for IS research has three advantages. First, the usage of IS can be investigated in a natural setting, which enables gathering new insights into practice and the generation of theories from practice. Second, the understanding of phenomena can be increased by its observation. Third, case studies can be used to investigate areas, which are currently not attended in IS research. New industry-relevant topics may arise, which are currently not regarded in research. Case studies provide valuable insights into such topics and investigation areas (Benbasat et al. 1987).

3.3 Research Process and Outcome

As outlined in Sect. 1.2, this book has four major research questions (RQ1–RQ4). Each question is answered by two or more research results, which are assigned to one of the four research areas (Compliance Management and IS, Business Process Compliance, Reporting Compliance, and Collaboration of IS and Legal Experts). Furthermore, each research result can be primarily assigned to one or more phases of the design science research methodology according to Peffers et al. (2007). Error! Reference source not found. depicts the assignment of each research result to its primarily addressed DSRM phases, and the applied research method(s). In the following, each research result, its applied research method, and its primarily affected DSRM phase is briefly outlined.

3.3.1 Compliance Management and Information Systems

The results in the first research area address the investigation of the impact of regulation on conceptual IS design (RQ1). First, the influence of regulation on the organization of IT departments and IT service providers is investigated by conducting a survey (Eggert et al. 2013b). A cross-industry study using Structural Equation Modeling (SEM) and Partial Least Squares (PLS) based on Contingency Theory (Donaldson 2001) is presented. Based on informed arguments, a research framework for model-based compliance management artifacts is developed (Becker et al. 2012c). Both research works can be primarily assigned to the problem identification and objective definition phases of the DSRM.

3.3.2 Business Process Compliance

The second block aims at improving the efficiency of business process compliance checking (RQ2). Therefore, an extensive literature review is conducted in order to identify research gaps in the state-of-the-art of business process compliance checking approaches (Becker et al. 2012a). Based on these findings, the design and development of a business process model checking approach that (1) is applicable on arbitrary modeling techniques and (2) for all kinds of compliance requirements (expressed as compliance patterns) is presented (Becker et al. 2011a). Further, the approach is demonstrated as a tool implementation. Based on the developed business process model checking approach, an evaluation method for compliance checking approaches, which partly applies considerations of the Technology Acceptance Model (TAM) (Davis et al. 1989; Venkatesh and Bala 2008) is presented (Becker et al. 2012e). The method is mainly discussed by informed arguments and can be assigned to the evaluation phase of the DSRM. Finally, the development process of the generalizable business process compliance checking approach plus a focus group interview-based evaluation at an IT service provider for financial institutes is discussed.

3.3.3 Reporting Compliance

The central goal of the research work in the reporting compliance section is to improve the modeling and analysis of regulatory report requirements (RQ3). First, the challenges of regulatory reporting for IS engineering are derived from two focus group interviews with experts from the financial industry. These qualitative results are matched with results from a structured literature review, which has led to a research agenda. In order to model regulatory reporting requirements for bank supervision, a multidimensional conceptual modeling technique is developed using method engineering and informed arguments. The modeling technique is evaluated with a laboratory experiment (Eggert et al. 2013a). In order to demonstrate the applicability of the modeling technique it is implemented into an adapted modeling tool based on the H2-Toolset (Fleischer 2013) (Becker et al. 2012d). The research work primarily makes use of a method engineering approach and informed arguments to express the relevance of the tool extensions for conceptual report analyses. It can be assigned to the DSRM phases design, development, and demonstration. Finally, the modeling technique is applied in three modeling projects for regulatory report requirements. In a laboratory experiment, students developed three extensive data warehouse models based on the modeling technique in order to demonstrate its feasibility (Becker et al. 2012b).

3.3.4 Collaboration of IS and Legal Experts

The overall research objective of the fourth research area is to conceptualize and to support the collaboration of IS and legal experts (RQ4). Three research artifacts have been developed in this area. A model-based framework for the collaboration of IS and legal experts in regulatory-driven IS projects provides insights into the practitioner perception of IS and law (Knackstedt et al. 2012). Based on a case study of an e-government case in Germany, the framework was developed and has led to design guidelines for further IS projects in a regulatory context. The relationship of IS research and law is then investigated in order to motivate a combined perspective in further IS research projects (Knackstedt et al. 2013). Based on an extensive literature review and informed arguments, the perceived relationship of IS and law is elaborated and discussed. Finally, the development of a research portal for legal informatics and information law provides a means for collaboration and for identifying research works in this evolving discipline (Knackstedt et al. 2010). Based on the informed argument approach, the portal is introduced and demonstrated.