Design Science Research Method Research Articles

Enterprise architecture approach for project-based organizations modeling, design, and analysis: An ontology-driven tool proposal

Project-based organizations (PBOs) rely on project management as their core business process. These organizations are characterized by their disaggregated structure and diverse authority distribution, leading to complications in synchronizing projects, governance, and functional divisions. In this regard, there is a need of a tool to manage this complexity. This paper introduces a novel approach – the IModel- for modeling, designing and analyzing PBOs, following the Design Science Research Method, that seeks to is creating an artifact that solves a real-world problem in a specific context. This artifact is developed through a conceptual integration between project management (PM) and enterprise architecture (EA) sources based on ontologies. Ontologies are powerful, machine-readable graph-structured models which enables sophisticated models’ reasoning and querying. IModel fuses the organizational domain from the ArchiMate EA language with PM insights from PMBOK 7th edition. The resulting IModel has been submitted to an expert’s judgement to assess its intended purposes as well, its potential to be applied in the real-world to support the PBO.

Digital Forensics Readiness Framework (DFRF) to Secure Database Systems

Database systems play a significant role in structuring, organizing, and managing data of organizations. In this regard, the key challenge is how to protect the confidentiality, integrity, and availability of database systems against attacks launched from within and outside an organization. To resolve this challenge, different database security techniques and mechanisms, which generally involve access control, database monitoring, data encryption, database backups, and strong passwords have been proposed. These techniques and mechanisms have been developed for certain purposes but fall short of many industrial expectations. This study used the design science research method to recommend a new Digital Forensic Readiness Framework, named DFRF, to secure database systems. DFRF involves risk assessments, data classification, database firewalls, data encryption, strong password policies, database monitoring and logging, data backups and recovery, incident response plans, forensic readiness, as well as education and awareness. The proposed framework not only identifies threats and responds to them more effectively than existing models, but also helps organizations stay fully compliant with regulatory requirements and improve their security. The design of the suggested framework was compared with existing models, confirming its superiority.

Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control

Digital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration of smart statistical digital systems into existing manufacturing control systems, exemplified with guidelines to transform an existent statistical process control into a smart statistical process control. Employing the design science research method, the research techniques include a literature review and interviews with experts who critically evaluated the proposed framework. The primary contribution lies in a set of general-purpose guidelines tailored to assist practitioners in manufacturing systems with the implementation of digital, smart technologies aligned with the principles of Industry 4.0. The resulting guidelines specifically target existing manufacturing plants seeking to adopt new technologies to maintain competitiveness. The main implication of the study is that practitioners can utilize the guidelines as a roadmap for the ongoing development and implementation of project management. Furthermore, the study paves the way for open innovation initiatives by breaking down the project into defined steps and encouraging individual or collective open contributions, which consolidates the practice of open innovation in manufacturing systems.

Design with Simon's Inner and Outer Environments: Theoretical Foundations for Design Science Research Methods for Digital Science

Design science research has traditionally been applied to complex real-world problems to produce an artifact to address such problems. Although design science research efforts have been applied traditionally to business or related problems, there is a large set of problems in the area of digital science that also require important, digital artifacts. The digitalization of science has resulted in the need to develop essential, specialized, devices and software before it is feasible for scientists to carry out their work. This research examines digital science to identify its challenges and demonstrate how it can be possible to progress digital science with design science research, thereby establishing digital science as an important area of transdisciplinary inquiry. These areas of research are examined for their synergies and explained by positioning artifact development challenges with respect to Simon's inner and outer environments and the interface between them.

Simulating staff activities in healthcare environments: An empirical multi-agent modeling approach

The layout of the healthcare environment profoundly impacts healthcare efficiency, safety, employee satisfaction, and the sustainability of operations, mainly based on occupant behavior analysis. However, due to the increase in professionalism and interprofessional collaboration among healthcare practitioners and the complexity of the physical environment, existing research tools face challenges in describing and analyzing diverse occupant behaviors. This study aims to offer an empirically grounded multi-agent modeling approach as a research tool to simulate and reproduce occupant behavior within healthcare settings. Employing a design science research method, the proposed approach encompasses four stages: empirical data collection, development of behavioral rules, agent-based modeling, and validation. A continuous 24-h observational study involving doctors, nurses, and auxiliary staff within a trauma intensive care unit is conducted to demonstrate the development and application process. Results show that the proposed modeling approach achieves a simulation deviation of 8.31 % in individual walking distances and a 16.26 % deviation in overall staff spatial visitation frequency compared to real-world observations. We believe that using simple if-then rules to represent events and probability rules to describe other basic activities can simulate complex staff activities in a healthcare environment. Interdisciplinary expert evaluation acknowledged the utility of this approach. This study offers a comprehensive dataset of behaviors and contributes methodologically to evaluating the efficiency of built environment layouts. The innovation in this study lies in its scalability, extending from events and individuals to encompass the all staff in levels of modeling and validation, ensuring a consistent alignment with empirical data at each stage.

Enhancing Organizational Culture and Productivity through Innovative Performance Appraisal: A Design Science Research Approach

Performance Appraisal is crucial for Human Resource Management (HRM) to determine compensation, enhance performance and develop competencies. Regular and comprehensive appraisals enable companies to devise effective strategies, boost productivity, and gain competitive advantage. The research aims to develop Performance Appraisal artifacts using Balanced Scorecard (BSC), 360°Feedback, and GE McKinsey 9 Box Matrix (GE Matrix), using Design Science (DS) research approach. This represents a novel contribution to the research. The implementation of the Design Science Research Method (DSRM) is described, emphasizing its focus on innovative information system designs to address practical organizational challenges. This study can be used as an additional reference for DS research on HRM.

Resolving power imbalances in construction payment using blockchain smart contracts

PurposeThis research aims to develop a blockchain smart contract–enabled framework to resolve power imbalance problems in construction payment.Design/methodology/approachThis research adopts a design science research method to develop the blockchain smart contract–enabled framework. The authors then develop a prototype system. Finally, the authors evaluate its performance in solving power imbalance-induced payment problems.FindingsThe results show that the prototype system can resolve power imbalance problems in construction payment by allowing project participants to make transparent and decentralized decisions that are self-enforceable by blockchain smart contracts.Research limitations/implicationsThis study provides theoretical explanations for how blockchain smart contracts can resolve power imbalances in construction payment; based on that, it proposes a novel blockchain smart contract–enabled method to rebalance the power of stakeholders in construction payment. Thus, it contributes to the body of knowledge on blockchain technology and construction payment.Practical implicationsThis study moves beyond a conceptual framework and develops a practical blockchain smart contract system for resolving power imbalances in construction payment, strengthening construction project members' confidence in using blockchain technology.Social implicationsThe proposed blockchain smart contract–enabled solution helps mitigate negative social impacts associated with late payment and non-payment. Furthermore, the research maximizes trust among participants in payment processes to inspire collaborative culture in the construction industry.Originality/valueThis paper introduces a novel blockchain smart contract integrated method, allowing project stakeholders to resolve power imbalance problems in construction payment through decentralized decision-making.

Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study.

Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site. This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.

The Circular Rebound Tool: A tool to move companies towards more sustainable circular business models

Companies design circular business models through experimentation. However, most companies do not consider the environmental impact of their new business model ideas during experimentation, an iterative phase of high uncertainty. Previous research shows that companies typically use ‘rules of thumb’ to estimate environmental impact in this stage due to limited time and reliable information to guide decision-making. This might prevent innovators from detecting unintended rebound effects that offset positive environmental gains of new business models. To mitigate this and let innovators think more profoundly about rebound effects during the circular business model experimentation phase, we propose an evidence-based business model ideation tool, the Circular Rebound Tool, designed around lifecycle thinking, the zero-waste hierarchy, and increased rebound effects awareness. The tool's development follows the design science research method, undergoing continuous improvement through 15 workshops. Our tool can help business innovators gain insights into the environmental impact of their early-stage business ideas.

Classifying enset based on their disease tolerance using deep learning

Even though agriculture remains the main source of Ethiopia’s population economy, not identifying enset based on their disease tolerance level is an obstacle for the production of enset.This paper’s main objective is to automatically identify the disease resistance levels of enset plants through digital image. The researcher followed the design science research method to achieve the objective listed above. Besides, the researcher has attempted to get valuable information about the type and the nature of these classes from the domain expert through interviews, document analysis, and observation from the fields. The total number of images used for experimentation purposes was 3000. The Contaharmonic filtering technique was implemented to remove noise due to its highest entropy recorded. A deep learning-based approach with training from scratch and transfer learning convolutional neural network methods were applied. Based on this, the researcher made experimentation for transfer learning by using two different pre-trained models, namely VGG-19 and VGG-16. Finally, the developed classifier model’s performance was assessed using accuracy, precision, recall, and the F1 score. According to the interpretation of the results, the proposed model’s training from scratch method achieves 92.6%. On the other way, the accuracy obtained with the transfer learning method, VGG-16 achieves 98.5%, and VGG-19 achieves 93.9%. Hence, transfer learning, specifically the VGG-16 model revealed an effective and robust performance for classifying enset based on their disease tolerance level based on the researcher’s number of images.

Integrating BIM–IoT and Autonomous Mobile Robots for Construction Site Layout Printing

The traditional methods of marking construction site layouts using manual techniques such as chalk lines are prone to human errors, resulting in discrepancies between blueprints and actual layouts. This has serious implications for project delivery, construction, costs and, eventually, project success. However, this issue can be resolved through autonomous robots and construction automation in line with Industry 4.0 and 5.0 goals. Construction automation enables workers to concentrate on the construction phase and not worry about manual site markups. This leads to an enhancement in their productivity. This study aims to improve the floor layout printing technique by introducing a framework that integrates building information modeling (BIM) and the Internet of Things (IoT), i.e., BIM–IoT and autonomous mobile robots (AMR). The development process focuses on three key components: a marking tool, an IoT-based AMR and BIM. The BIM-based tools extract and store coordinates on the cloud platform. The AMR, developed using ESP32 and connected to the Google Firestore cloud platform, leverages IoT technology to retrieve the data and draw site layout lines accordingly. Further, this research presents a prototype of an automated robot capable of accurately printing construction site layouts. A design science research (DSR) method is employed in this study that includes a comprehensive review of the existing literature and usage of AMRs in construction layout printing. Subsequently building upon the extant literature, an AMR is developed and experiments are conducted to evaluate the system’s performance. The experiment reveals that the system’s precision falls within a range of ±15 mm and its angle accuracy is within ±4 degrees. Integrating robotic automation, IoT and BIM technologies enhances the efficiency and precision of construction layout printing. The findings provide insights into the potential benefits of deploying AMRs in construction projects, reducing site layout errors and improving construction productivity. This study also adds to the body of knowledge around construction automation in line with Industry 4.0 and 5.0 endeavors.

RESÍDUOS DE DENIN: experiências de reutilização em confecção de jeans

Planning the use of techniques to reuse materials that are wasted can help to reduce environmental damage or even avoid it. This article presents the results of a research that explored the denim lefto-vers in a jeans factory, as well as the proposal for the reuse of waste through sustainable alternatives, generating possibilities for the development of new products, from the reuse of denim discarded. For the development of the research, the Design Science research method was used, following the following steps: understanding the problem, generation of alternatives, development of artifacts, evaluation and conclusions. Pieces were prototyped from reuse, with six pairs of shoes and six bags. The results achieved showed that the reuse of denim waste associated with design elements are ways to innovate, making fashion more sustainable and ethical, adding value to the product, through resignification.

Transformasi Digital InsurCo dengan Merancang Pengelolaan Risiko Teknologi Informasi Menggunakan Framework COBIT 2019 IT Risk Management Focus Area

Rapid advancements in Information Technology (IT) play a vital role in fostering innovation within a company. Previous research has successfully identified the significance of IT Governance and Management (ITGM) in the process of Digital Transformation (DT). However, a more comprehensive analysis is needed to understand the role of IT risk management in supporting such changes, particularly through a case study on the insurance company InsurCo. This study employs the Design Science Research (DSR) method, utilizing interview data and the COBIT 2019 IT Risk Management Focus Area framework to design an IT risk management approach that supports InsurCo's DT. Capability assessments, gap analysis, potential improvements, improvement designs, roadmap development, and impact estimation of implementation have been executed on seven ITGM components. InsurCo, being a State-Owned Enterprise, is bound by the regulation PER-2/MBU/03/2023 concerning the Guidelines for Governance and Significant Corporate Activities of State-Owned Enterprises. Additionally, the Financial Services Sector Master Plan 2021-2025 highlights the acceleration of DT. The prioritized ITGM objectives required by InsurCo are derived from three design factor determinations: focus areas, design mechanisms, and ITGM mechanisms concerning DT, resulting in the following prioritized ITGM objectives: APO12 Managed Risk, EDM03 Ensure Risk Optimization, and DSS04 Managed Continuity. There is an estimated average capability increase of 3.57% across these three ITGM objectives. The research outcome presents a recommended design for the seven components of the governance system, aiming to enhance the IT risk management capability index within InsurCo. This study contributes to a deeper understanding of the concept of prioritizing IT risk management to support organizational digital transformation, specifically providing practical implications for InsurCo to bolster the success of their strategic programs, and benefit the insurance industry.

Brazilian agtech scalability: proposal and assessment of a local multidimensional model

PurposeThis study aims to propose and assess a model with the main elements influencing Brazilian agtech scalability with the aim of supporting the scalability process of startups that find themselves in the stage of initial conception of the business model.Design/methodology/approachThe research was conducted using the design science research (DSR) method. The data for assessing the proposed model were collected through in-depth interviews. The answers were analyzed quantitatively, using descending hierarchical classification (DHC), correspondence factor analysis (CFA), and level of agreement; and qualitatively, using content analysis.FindingsConsidering the Brazilian context, the objective, environmental, structural, and evaluative dimensions presented positive characteristics for the main criteria analyzed (operational viability, generality, clarity, adaptation to the reality studied, completeness, consistency, comprehensibility, and structural simplicity). Specific improvements were proposed in all the criteria analyzed.Originality/valueThe artifact can be considered a strategic guide for agtechs that have yet to overcome the barrier of initial conception of the business model. The model enables the identification of the main problems that agtechs can encounter in their life cycle, as well as seeking solutions in advance.

A Dynamic and Adaptive Cybersecurity Governance Framework

Cybersecurity protects cyberspace from a wide range of cyber threats to reduce overall business risk, ensure business continuity, and maximize business opportunities and return on investments. Cybersecurity is well achieved by using appropriate sets of security governance frameworks. To this end, various Information Technology (IT) and cybersecurity governance frameworks have been reviewed along with their benefits and limitations. The major limitations of the reviewed frameworks are; they are complex and have complicated structures to implement, they are expensive and require high skill IT and security professionals. Moreover, the frameworks require many requirement checklists for implementation and auditing purposes and a lot of time and resources. To fill the limitations mentioned above, a simple, dynamic, and adaptive cybersecurity governance framework is proposed that provides security related strategic direction, ensures that security risks are managed appropriately, and ensures that organizations’ resources are utilized optimally. The framework incorporated different components not considered in the existing frameworks, such as research and development, public-private collaboration framework, regional and international cooperation framework, incident management, business continuity, disaster recovery frameworks, and compliance with laws and regulations. Moreover, the proposed framework identifies and includes some of the existing frameworks’ missed and overlapped components, processes, and activities. It has nine components, five activities, four outcomes, and seven processes. Performance metrics, evaluation, and monitoring techniques are also proposed. Moreover, it follows a risk based approach to address the current and future technology and threat landscapes. The design science research method was used in this research study to solve the problem mentioned. Using the design science research method, the problem was identified. Based on the problem, research objectives were articulated; the objective of this research was solved by developing a security governance framework considering different factors which were not addressed in the current works. Finally, performance metrics were proposed to evaluate the implementation of the governance framework.

Optimization of the communication processes and knowledge management in retail teams

The success of innovation is due to the relationship within innovation teams. In this context, communication is a fundamental tool to add value to organizations. Therefore. A system to optimize communication within these teams is growing in importance. So, This study aims to propose an artifact to optimize the knowledge management and communication process flows in teams from organizations in the retail sector. Likewise, it was possible to comprehend the influence and the importance of these processes for the development of innovation in businesses. The Design Science Research method was used, which culminated in the creation of an artifact for better use of communicational interactions. So, the created solution also went through an evaluation stage that allowed a full analysis of its behaviors and usefulness. Finally, it was concluded that the proposed artifact is of extreme importance for organizations, once that, with its results, it can directly impact team productivity and boost the innovative process within companies. As a contribution, the proposed artifact includes three new phases in the project cycle that are placed between each of the existing phases, called "project story" and aims to tell the work in detail. 

The mechanism of retired power batteries recycling through blockchain token incentives

With the development of the electric vehicle industry, an increasing number of power batteries are reaching retirement. It is essential to return retired power batteries to formal recycling channels. To tackle the shortcomings in combating unqualified recyclers in traditional government subsidy and target responsibility models, the paper adopts the design science research method and proposes a novel approach that combines the roles of government and the market. Firstly, a token incentive mechanism is introduced and designed. Secondly, the shortcomings of the traditional models are analyzed, and the advantages of the token incentive models are highlighted. Finally, we examine the influence of different models on the manufacturer profits and social benefits. The results show that the token incentive model can combat the unqualified recyclers effectively, but the manufacturers will pay more cost within their capacity to bear expenses. In addition, the token incentive model yields more significant.significant social benefits.

Data management system for sustainable agriculture among smallholder farmers in Tanzania: research-in-progress

ABSTRACT Smallholder farmers produce about 70% of the world’s food and employ more than one billion people. They therefore have an important role to play in eradicating food insecurity and poverty among the world’s growing population. Although there are different digital services for smallholder farmers, the existing services lack sustainability in the agriculture context and hardly meet their needs. Data management and sharing among different agriculture stakeholders has the potential to make agriculture sustainable, but there is a need to enable access to digital services in an entire farming cycle under one roof. This paper aims to propose the design of a comprehensive data management digital framework to solve common challenges of smallholder farmers in Tanzania and other countries’ agricultural systems. We follow the design science research (DSR) method to develop an artifact that interacts with the problem context. To illustrate the framework’s applicability, we use different case studies in Tanzania.

Developing design principles for the implementation of AI in PSM: An investigation with expert interviews

The use of artificial intelligence (AI) in purchasing and supply management (PSM) has great potential, e.g. for automating processes and supporting PSM employees. Nevertheless, the number of realized AI use cases in PSM is still limited, and mostly in large companies. This paper examines first requirements for the implementation of AI in PSM. Second, design principles are developed on the basis of the identified requirements. Third, exemplary AI use cases in PSM are presented. For this purpose, a literature analysis is first conducted and followed by interviews with 17 PSM and technology experts. Finally, 40 requirements for the implementation of AI in PSM are developed from the conducted expert interviews. Based on these 40 requirements, ten design principles are developed in the sense of the design science research (DSR) approach to describe the relevant activities for the implementation of AI in PSM. The artifacts developed in this paper will contribute to the implementation of AI in PSM. The applicable research results should facilitate the transfer of knowledge into practice and contribute to the establishment of the DSR method in PSM research.

Exploring the Role of Federated Data Spaces in Implementing Twin Transition within Manufacturing Ecosystems.

Globally, manufacturing ecosystems are facing the challenge of twin transition, i.e., how to utilize digitalization for improving or transforming the sustainability of manufacturing operations. Here, operations refer widely to the upstream of manufacturing, while the entire product lifecycle also covers the downstream and end-of-life operations. Here, sustainability is understood to consider the impact of the product lifecycle at environmental, social, and governance (ESG) levels. In this article, we explore this progress through the digitalization concept of business-to-business data sharing, and through one example of a manufacturing ecosystem in Finland. We discuss the federated data space concept and the international data spaces (IDS) architecture as technological building blocks of twin transition, and report the first results from an industry-research shared-risk project. Semi-structured interviews and a diary-style reporting from an industry-research IDS proof-of-concept (PoC) experiment are presented and analyzed within a design science research method framework. The findings give the first indications that while data sharing is seen as important and increasing in relevance in industry, it is currently challenging for companies to see how an open standard architecture creates value beyond a single limited ecosystem view. We also highlight possible avenues for further research.