Design Science Research in Information Systems: A Systematic Literature Review 2001-2015

In the last few years, design science research has received wide attention within the IS community. It is increasingly recognized as an equal companion to IS behavioral science research and being applied to address IS topics. With the aim of providing an overview of its current state, this paper presents a systematic literature review on design science research in IS field. The results of this paper reveal the focuses of previous theoretical and empirical design science research and provide some directions for future IS design science research

OntoEng: A design method for ontology engineering in information systems

This paper addresses the design problem relating to ontology engineering in the discipline of information systems. Ontology engineering is a realm that covers issues related to ontology development and use throughout its life span. Nowadays, ontology as a new innovation promises to improve the design, semantic integration, and utilization of information systems. Ontologies are the backbone of knowledge-based systems. In addition, they establish sharable and reusable common understanding of specific domains amongst people, information systems, and software agents. Notwithstanding, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. On the basis of the gathered experience during the development of V4 Telecoms Business Model Ontology as well as the conducted integration of the related literature from the design science paradigm, this paper introduces OntoEng and its application as a novel systematic design method for ontology engineering

A Survey of the Application of Machine Learning in Decision Support Systems

Machine learning is a useful technology for decision support systems and assumes greater importance in research and practice. Whilst much of the work focuses technical implementations and the adaption of machine learning algorithms to application domains, the factors of machine learning design affecting the usefulness of decision support are still understudied. To enhance the understanding of machine learning and its use in decision support systems, we report the results of our content analysis of design-oriented research published between 1994 and 2013 in major Information Systems outlets. The findings suggest that the usefulness of machine learning for supporting decision-makers is dependent on the task, the phase of decision-making, and the applied technologies. We also report about the advantages and limitations of prior research, the applied evaluation methods and implications for future decision support research. Our findings suggest that future decision support research should shed more light on organizational and people-related evaluation criteria

Design Artifact to Support Knowledge-Driven Predictive and Explanatory Decision Analytics

In this paper, we develop a novel design artifact to support knowledge-driven predictive and explanatory decision analytics for a complex business process. Following Design Science research guidelines in Hevner et al, (2004), we show the development of the design artifact and evaluate the artifact’s effectiveness in providing intelligent decision support for a complex business process. We present a design artifact that provides predictive and explanatory analytics to support intelligent decision-making. We use a large, automated, continuous manufacturing process as the problem domain where the continual monitoring of the process using knowledge- driven, intelligent tools is useful for process monitoring and quality control problems. This research contributes to design science by explicating a novel artifact with predictive and explanatory features that are useful in intelligent systems design. It provides sophisticated and adaptable intelligent decision support to the problem domain of process control

The design and engineering of innovative mobile data services: An ontological framework founded on business model thinking

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research investigates mobile service design and engineering in the mobile telecommunications industry. The mobile telecommunication business is shifting from one that was voice-centric to one that is almost all data-centric; thanks to recent rapid advances in Information and Communication Technologies (ICTs). The underlying reasons behind this shift can be traced back to two main issues that are interlinked. The first and major reason is that telecoms (telecommunication companies) are trying to generate new revenue streams based on data and information transmissions, given the saturation of the voice market. This is rational given the market opportunities in one direction and the pressures being generated by the current economic downturn from the other direction. The second reason relates to the flexibility of data, compared to voice. Indeed, the number of services that can be developed on the basis of data are much greater than those that can be developed on the basis of voice. However, the design and engineering of successful and innovative mobile data services has proven to be a complex undertaking. The number of effective mobile data services is relatively small and the revenue generated from such offerings has generally been below expectations. This research develops an ontological framework to help in changing this situation, and making mobile services engineering more effective and successful, following the design-science research paradigm. Design-science research, in general, aims to solve unstructured but relevant organizational or social problems through the development of novel and useful artefacts. As the current research aims to help in solving the mobile data services engineering dilemma by developing a purposeful ontological framework, the design-science research paradigm is deemed fitting. Within this paradigm, the author develops a novel design approach specified for ontology engineering, termed “OntoEng”. This design approach is used in this research for developing the ontological framework. The developed ontological framework is founded on business model thinking. The idea is that creating innovative mobile data services requires developing innovative business models. Indeed, innovative business models can help translate technological potential into economic value and allow telecoms to achieve their strategic objectives. The ontological framework includes the development of an ontology, termed “V4 Mobile Service BM Ontology” as well as “Mobile Key Value Drivers” for designing and engineering innovative mobile data services. The V4 Mobile Service BM Ontology incorporates four design dimensions: (1) value proposition including targeting; (2) value architecture including technological and organizational infrastructure; (3) value network dealing with aspects relating to partnerships and co-operations; and finally (4) value finance relating to costs, pricing, and revenue structures. Within these four dimensions, sixteen design concepts are identified along with their constituent elements. Relationships and interdependencies amongst the identified design constructs are established and clear semantics are produced. The research then derives six key value drivers for mobile service engineering as follows: (a) Market Alignment; (b) Cohesion; (c) Dynamicity; (d) Uniqueness; (e) Fitting Network-Mode; and (f) Explicitness. The developed ontological framework in this research is evaluated to ensure that it can be successfully implemented and performs correctly in the real world. The research mainly utilizes case analysis methods to ensure the semantic correctness of the ontological framework. Indeed, the developed ontological framework is employed as an analytical lens to examine the design and engineering of three key real-life cases in the mobile telecommunications industry. These cases are: (1) Apple’s iPhone Services and Applications; (2) NTT DoCoMo’s i-mode Services; and (3) Orange Business Services. For further validation, the developed ontological framework is evaluated against a set of criteria synthesized from ontology engineering and evaluation literature. These criteria are: Clarity; Coherence; Conciseness; Preciseness; Completeness; and Customizability. The developed ontological framework is argued to make significant contributions for theory, practice, and methodology. For theory, this research provides (1) a novel ontological framework for designing and engineering mobile data services; (2) a unified framework of the business model concept; and (3) a new design approach for ontology engineering in information systems. For practice, the current research provides practitioners in the telecommunications industry with systematic and customizable means to design, implement, analyze, evaluate, and change new and existing mobile data services to make them more manageable, effective, and creative. For methodology, the use of the design- science research paradigm for ontology engineering signifies the focal methodological contribution in this research given its novelty. This research also contributes to the understanding of the design-science research paradigm in information systems as it is relatively new. It provides a working example in which the author illustrates how recognizing design-science research as a paradigm is essential and useful to the research in information systems discipline

The design and engineering of innovative mobile data services : an ontological framework founded on business model thinking

This research investigates mobile service design and engineering in the mobile telecommunications industry. The mobile telecommunication business is shifting from one that was voice-centric to one that is almost all data-centric; thanks to recent rapid advances in Information and Communication Technologies (ICTs). The underlying reasons behind this shift can be traced back to two main issues that are interlinked. The first and major reason is that telecoms (telecommunication companies) are trying to generate new revenue streams based on data and information transmissions, given the saturation of the voice market. This is rational given the market opportunities in one direction and the pressures being generated by the current economic downturn from the other direction. The second reason relates to the flexibility of data, compared to voice. Indeed, the number of services that can be developed on the basis of data are much greater than those that can be developed on the basis of voice. However, the design and engineering of successful and innovative mobile data services has proven to be a complex undertaking. The number of effective mobile data services is relatively small and the revenue generated from such offerings has generally been below expectations. This research develops an ontological framework to help in changing this situation, and making mobile services engineering more effective and successful, following the design-science research paradigm. Design-science research, in general, aims to solve unstructured but relevant organizational or social problems through the development of novel and useful artefacts. As the current research aims to help in solving the mobile data services engineering dilemma by developing a purposeful ontological framework, the design-science research paradigm is deemed fitting. Within this paradigm, the author develops a novel design approach specified for ontology engineering, termed “OntoEng”. This design approach is used in this research for developing the ontological framework. The developed ontological framework is founded on business model thinking. The idea is that creating innovative mobile data services requires developing innovative business models. Indeed, innovative business models can help translate technological potential into economic value and allow telecoms to achieve their strategic objectives. The ontological framework includes the development of an ontology, termed “V4 Mobile Service BM Ontology” as well as “Mobile Key Value Drivers” for designing and engineering innovative mobile data services. The V4 Mobile Service BM Ontology incorporates four design dimensions: (1) value proposition including targeting; (2) value architecture including technological and organizational infrastructure; (3) value network dealing with aspects relating to partnerships and co-operations; and finally (4) value finance relating to costs, pricing, and revenue structures. Within these four dimensions, sixteen design concepts are identified along with their constituent elements. Relationships and interdependencies amongst the identified design constructs are established and clear semantics are produced. The research then derives six key value drivers for mobile service engineering as follows: (a) Market Alignment; (b) Cohesion; (c) Dynamicity; (d) Uniqueness; (e) Fitting Network-Mode; and (f) Explicitness. The developed ontological framework in this research is evaluated to ensure that it can be successfully implemented and performs correctly in the real world. The research mainly utilizes case analysis methods to ensure the semantic correctness of the ontological framework. Indeed, the developed ontological framework is employed as an analytical lens to examine the design and engineering of three key real-life cases in the mobile telecommunications industry. These cases are: (1) Apple’s iPhone Services and Applications; (2) NTT DoCoMo’s i-mode Services; and (3) Orange Business Services. For further validation, the developed ontological framework is evaluated against a set of criteria synthesized from ontology engineering and evaluation literature. These criteria are: Clarity; Coherence; Conciseness; Preciseness; Completeness; and Customizability. The developed ontological framework is argued to make significant contributions for theory, practice, and methodology. For theory, this research provides (1) a novel ontological framework for designing and engineering mobile data services; (2) a unified framework of the business model concept; and (3) a new design approach for ontology engineering in information systems. For practice, the current research provides practitioners in the telecommunications industry with systematic and customizable means to design, implement, analyze, evaluate, and change new and existing mobile data services to make them more manageable, effective, and creative. For methodology, the use of the design- science research paradigm for ontology engineering signifies the focal methodological contribution in this research given its novelty. This research also contributes to the understanding of the design-science research paradigm in information systems as it is relatively new. It provides a working example in which the author illustrates how recognizing design-science research as a paradigm is essential and useful to the research in information systems discipline.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Social media enabled collaborative learning environments: a design science research approach

Collaborative technologies such as Group Decision Support Systems were proclaimed to be able to impact the learning environments of educational institutions twenty years ago, where the Information Systems discipline was interested in determining whether they were capable of transforming the traditional methods of teaching. It was understood that these technologies were effective at transforming learning environments from a traditional approach to a collaborative one, where the learner is part of the learning process, but little has actually changed in this time. However, new generations of these collaborative technologies often emerge, and the platforms of social media are one such technology. In a similar fashion to previous collaborative technologies, social media have been proclaimed as impacting the learning environments of educational institutions through better communication and collaboration, in new and exciting ways. However, a problem that has been identified is there is a lack of understanding on whether the platforms that are enabled by social media are effective at enabling collaborative learning. This study helps improve this understanding. A design science research (DSR) approach was adopted to build an evaluation framework to be able to evaluate the effectiveness of social media enabled collaborative learning environments (SMECLEs). The evaluation framework was developed during a five year DSR study, over six design cycles. These incorporated insights from existing literature on DSR, social media, and collaborative learning, using 272 journal and conference articles. Further, data was gathered from six SMECLEs, which consisted of 857 tweets, 1439 blog posts, and 3376 blog comments. The resulting framework was then used to evaluate the six SMECLEs, where a number of trends were identified, which suggests that the tool is effective for its intended purpose. Thus, the primary contribution of this study, to both practice and the knowledge base, is the evaluation framework for social media enabled collaborative learning environments (SMECLEs)