scholarly journals Design Science Research: Evaluation in the Lens of Big Data Analytics

Systems ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 27 ◽  
Author(s):  
Ahmed Elragal ◽  
Moutaz Haddara
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Design Research ◽  
Research Evaluation ◽  
Design Science ◽  
Big Data Analytics ◽  
Science Research ◽  
Design Science Research ◽  
Evaluation Phase ◽  
New Type

Given the different types of artifacts and their various evaluation methods, one of the main challenges faced by researchers in design science research (DSR) is choosing suitable and efficient methods during the artifact evaluation phase. With the emergence of big data analytics, data scientists conducting DSR are also challenged with identifying suitable evaluation mechanisms for their data products. Hence, this conceptual research paper is set out to address the following questions. Does big data analytics impact how evaluation in DSR is conducted? If so, does it lead to a new type of evaluation or a new genre of DSR? We conclude by arguing that big data analytics should influence how evaluation is conducted, but it does not lead to the creation of a new genre of design research.

Design science research with focus groups – a pragmatic meta-model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Telmo Antonio Henriques ◽  
Henrique O’Neill
Keyword(s):  
Focus Groups ◽  
Design Research ◽  
Design Science ◽  
Science Research ◽  
Design Science Research ◽  
Success Factor ◽  
Content Type ◽  
Meta Model ◽  
Is Implementation ◽  
Practical Guidelines

PurposeThe purpose of this research paper is to present a pragmatic and systematic approach to conduct and document Design Science Research (DSR) activities with Focus Groups (FGs), exploring its continuous usage and providing traceability between problem, requirements, solutions and artefacts.Design/methodology/approachThe approach is to conduct the research and produce the meta-model for DSR with FG, a DSR approach was adopted using a conceptual model for Action Design Research already available. The artefact is the result from a specific literature review to define requirements, a careful design and a refinement stage where it was widely used and tested in real IS implementation projects.FindingsRigorous and committed stakeholder engagement is a critical success factor in complex projects. The main outcome of this research is a specific meta-model for DSR with FG that delivers new insights and practical guidelines for academics and professionals conducting and documenting real-world research and development initiatives deep-rooted in stakeholders' participation.Research limitations/implicationsThe meta-model has been endorsed as a practical and useful artefact by the stakeholders participating in the IS projects where it was adopted. However, to fully demonstrate its capabilities and to become more robust, the model has to be further used and tested in other application situations and environments.Originality/valueThe usage of FGs in DSR has already been proposed as an effective way, either to study artefacts, to propose improvements in its design or to acknowledge the utility of those artefacts in field use. The paper provides a sound contribution to this line of research by presenting a meta-model that integrates process and data, as well as a set of practical templates and forms that may be used by researchers and practitioners to conduct their projects.

scholarly journals Big Data Analytics Correlation Taxonomy

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Laden Husamaldin ◽  
Nagham Saeed
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Design Research ◽  
Big Data Analytics ◽  
Research Area ◽  
Analytic Methods ◽  
Lack Of Information ◽  
The Difference ◽  
Type Data ◽  
Research Account

Big data analytics (BDA) is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. In the literature, researchers have focused on classifying big data according to data type, data security or level of difficulty, and many research papers reveal that there is a lack of information on evidence of a real-world link of big data analytics methods and its associated techniques. Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniques. Therefore, this paper gives a design research account for formulating and proposing a step ahead to understand the relation between the analytical methods and its associated techniques. Furthermore, this paper is an attempt to clarify this uncertainty and identify the difference between analytics methods and techniques by giving clear definitions for each method and its associated techniques to integrate them later in a new correlation taxonomy based on the research approaches. Thus, the primary outcome of this research is to achieve for the first time a correlation taxonomy combining analytic methods used for big data and its recommended techniques that are compatible for various sectors. This investigation was done through studying various descriptive articles of big data analytics methods and its associated techniques in different industries.

scholarly journals Towards Participatory Action Design Research: Adapting Action Research and Design Science Research Methods for Urban Informatics

2011 ◽  
Vol 7 (3) ◽  
Author(s):  
Mark Bilandzic ◽  
John Venable
Keyword(s):  
Action Research ◽  
Design Research ◽  
Design Science ◽  
Science Research ◽  
Design Science Research ◽  
Urban Environments ◽  
Participatory Action ◽  
Action Design Research ◽  
Urban Informatics ◽  
Research And Design

This paper proposes a new research method, Participatory Action Design Research (PADR), for studies in the Urban Informatics (UI) domain. PADR supports UI research in developing new technological means (e.g. using mobile and ubiquitous computing) to resolve contemporary issues or support everyday life in urban environments. Situated in a socio-technical context, UI requires a close dialogue between social and design-oriented fields of research as well as their methods. PADR combines Action Research and Design Science Research, both of which are used in Information Systems, another field with a strong socio-technical emphasis, and further adapts them to the cross-disciplinary needs and research context of UI.

scholarly journals Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations

2019 ◽  
Vol 12 (1) ◽  
pp. 277 ◽  
Author(s):  
Vinicius Luiz Ferraz Minatogawa ◽  
Matheus Munhoz Vieira Franco ◽  
Izabela Simon Rampasso ◽  
Rosley Anholon ◽  
Ruy Quadros ◽  
...  
Keyword(s):  
Big Data ◽  
Performance Management ◽  
Business Model ◽  
Small Businesses ◽  
Data Analytics ◽  
Design Science ◽  
Big Data Analytics ◽  
Business Model Innovation ◽  
Raw Material ◽  
Animal Origin

Business model innovation is considered key for organizations to achieve sustainability. However, there are many problems involving the operationalization of business model innovation. We used a design science methodology to develop an artifact to assist business model innovation efforts. The artifact uses performance measurement indicators of the company’s business model, which are powered by Big Data analytics to endow customer-driven business model innovation. Then, we applied the artifact in a critical case study. The selected company is a fashion ecommerce that proposes a vegan and sustainable value using recycled plastic bottle yarn as raw material, and ensures that no material with animal origin is used. Our findings show that the artifact successfully assists a proactive and continuous effort towards business model innovation. Although based on technical concepts, the artifact is accessible to the context of small businesses, which helps to democratize the practices of business model innovation and Big Data analytics beyond large organizations. We contribute to the business model innovation literature by connecting it to performance management and Big Data and providing paths for its operationalization. Consequently, in practice, the proposed artifact can assist managers dealing with business model as a dynamic element towards a sustainable company.

scholarly journals A design science nas pesquisas em design no Brasil

2020 ◽  
Vol 28 (1) ◽  
Author(s):  
Iana Uliana Perez ◽  
Mônica Moura ◽  
Fausto Orsi Medola
Keyword(s):  
Design Research ◽  
Design Science ◽  
Science Research ◽  
Design Science Research ◽  
Action Design Research

Este artigo apresenta a abordagem da design science como alternativa para as investigações em design e nas ciências sociais aplicadas em geral. Para a sua redação, foi empreendida Revisão Bibliográfica Sistemática no Catálogo de Teses e Dissertações da Capes, verificando-se a incidência dos termos “design science” e “action design research”. O levantamento permitiu a identificação de 14 teses e dissertações em design que adotaram métodos próprios da design science, como Design Science Research (DSR) e Action Design Research (ADR). Para caracterizar esses métodos, é apresentada análise comparativa das pesquisas de três teses que adotaram a DSR; também é relatada a experiência de realização de uma pesquisa de mestrado que utilizou a ADR. A discussão ressalta as contribuições desses métodos e os aspectos que precisam ser aprimorados para sua operacionalização.

scholarly journals Lean Based Maturity Framework Integrating Value, BIM and Big Data Analytics: Evidence from AEC Industry

2021 ◽  
Vol 13 (18) ◽  
pp. 10029
Author(s):  
Gökhan Demirdöğen ◽  
Nihan Sena Diren ◽  
Hande Aladağ ◽  
Zeynep Işık
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Negative Impact ◽  
Design Science ◽  
Big Data Analytics ◽  
Value Added ◽  
Science Research ◽  
Research Problem ◽  
Design Cycle ◽  
Aec Industry

The construction industry is considered as one of the least productive, highest energy consuming, and least digitized industries. The Lean Management (LM) philosophy became a significant way for eliminating non-value-added activities and wastes during a building’s lifecycle. However, studies have shown that philosophies are not efficient by themselves to solve the issues of the construction industry. They need to be supported with the appropriate technologies and tools. Therefore, the integrated use of Building Information Modelling (BIM) with LM or Value Engineering (VE) were proposed in the literature. Nonetheless, it was also seen that BIM can provide more insights and improvements when BIM is integrated with data analysis tools to analyze BIM data. In the literature, the synergies between these concepts are generally addressed pairwise, and there is no comprehensive framework which identifies their relationships. Therefore, this study aims to develop a maturity framework that facilitates the adoption of LM, VE, BIM, and Big Data Analytic (BDA) concepts to address long-standing productivity and digitalization issues in the Architecture, Engineering, and Construction (AEC) industry. Design Science Research (DSR) methodology and its three-cycle view (relevance, rigor, and design cycle) were applied to build the proposed maturity framework. Two interviews were performed to identify and observe research problem in relevance cycle. In the rigor cycle, a comprehensive literature review was performed to create a base for the development of the maturity framework. In addition to the developed base of the framework, lean processes were added to this cycle. In the design cycle, the developed framework was evaluated and validated by five experts through face-to-face interviews. The importance of employer’s requirements to adopt the proposed methodologies, the negative impact of change orders, the importance of pre-construction phases to facilitate value creation and waste elimination, and the usage of common data environment with BIM were identified as the prominent application and adaptation issues.

scholarly journals A Conceptual Culture Model for Design Science Research

2016 ◽  
Vol 6 (3) ◽  
pp. 01
Author(s):  
Thomas Richter
Keyword(s):  
Design Research ◽  
Theoretical Foundation ◽  
Design Science ◽  
Science Research ◽  
Design Science Research ◽  
Social Environments ◽  
Culture Dimensions ◽  
Culture Model ◽  
Culture Models ◽  
Artifact Design

<p>The aim of design science research (DSR) in information systems is the user-centred creation of IT-artifacts with regard to specific social environments. For culture research in the field, which is necessary for a proper localization of IT-artifacts, models and research approaches from social sciences usually are adopted. Descriptive dimension-based culture models most commonly are applied for this purpose, which assume culture being a national phenomenon and tend to reduce it to basic values. Such models are useful for investigations in behavioural culture research because it aims to isolate, describe and explain culture-specific attitudes and characteristics within a selected society. In contrast, with the necessity to deduce concrete decisions for artifact-design, research results from DSR need to go beyond this aim. As hypothesis, this contribution generally questions the applicability of such generic culture dimensions’ models for DSR and focuses on their theoretical foundation, which goes back to Hofstede’s conceptual Onion Model of Culture. The herein applied literature-based analysis confirms the hypothesis. Consequently, an alternative conceptual culture model is being introduced and discussed as theoretical foundation for culture research in DSR.</p><p> </p>

Big data analytics and demand forecasting in supply chains: a conceptual analysis

2018 ◽  
Vol 29 (2) ◽  
pp. 739-766 ◽  
Author(s):  
Erik Hofmann ◽  
Emanuel Rutschmann
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Conceptual Analysis ◽  
Scientific Literature ◽  
Design Science ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Scientific Work ◽  
Content Type ◽  
Forecasting Methods

Purpose Demand forecasting is a challenging task that could benefit from additional relevant data and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances forecasts’ accuracy. Design/methodology/approach A conceptual structure based on the design-science paradigm is applied to create categories for BDA. Existing approaches from the scientific literature are synthesized with industry knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps: description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the interplay between elements, and creation of a matching by conducting investigations within the retail industry. Findings The developed framework could serve as a guide for meaningful BDA initiatives in the supply chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but requires data scientists to perform the job, an appropriate technological foundation, and technology investments. Originality/value So far, no scientific work has analyzed the relation of forecasting methods to BDA; previous works have described technologies, types of analytics, and forecasting methods separately. This paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their operational, tactical, or strategic demand plans.

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