Design Science Paradigm Research Articles

Towards a comprehensive framework to support project studies in the context of university research centres: a design science research

PurposeKnowledge production in project studies is continuously challenged to combine scientific rigour and practical relevance, and a professional graduate programme in Project Management is a suitable environment for addressing this issue. This research aims to generate a framework of the Ecosystem of a University Research Centre in Project Studies (URC-PS) to enhance the benefits of research developed in a professional graduate programme.Design/methodology/approachThe research was developed under the paradigm of Design Science and operationalised through a method of Design Science Research. The prescriptive approach was used to identify, design, develop, demonstrate, and evaluate the framework.FindingsThe framework comprises four macro-elements: Project Studies; Impact Generation Process; Circumstances, Governance and Management; and Context, broken into sixty elements. It provides a structure that is simultaneously holistic, integrative, and procedural. It also develops a perspective of knowledge co-creation between academics and practitioners in an engaged scholarship approach.Practical implicationsThe framework provides a more thorough understanding of the ecosystem university management to the research centre itself, to engaged academics, and to external actors, which allows them to discuss, plan, execute, and evaluate the co-creation of knowledge in Project Studies.Originality/valueThe framework contributes to Organisational Knowledge Creation Theory by including and discussing outcomes and impacts from co-created knowledge in a URC-Project Studies environment. It also explores the concept of “Ba” in its proposal for structuring, organising, and operationalising the “Ba”.

Creating a Topic Map for representing Network Security Competencies

Competencies represent the knowledge, skills and attitudes required for job roles. Organisations need to understand and grow competencies within their workforce in order to be more competitive and to maximise new market opportunities. The assessment of competencies can be conceptualised from two perspectives. The first is ‘Competency Frameworks’, which describe competencies from a high-level overview. The second perspective is ‘Competency-Based Learning’, which focuses on addressing competencies from a more detailed, task-oriented perspective. This research presents a tool that can map these two perspectives together. This mapping provides a more holistic approach to describing competencies, which is particularly important to network security occupational job roles. In this research, the Design Science paradigm was used as the methodology for creating and evaluating a topic map.

Creating Proactive Cyber Threat Intelligence with Hacker Exploit Labels: A Deep Transfer Learning Approach

The rapid proliferation of complex information systems has been met by an ever-increasing quantity of exploits that can cause irreparable cyber breaches. To mitigate these cyber threats, academia and industry have placed a significant focus on proactively identifying and labeling exploits developed by the international hacker community. However, prevailing approaches for labeling exploits in hacker forums do not leverage metadata from exploit darknet markets or public exploit repositories to enhance labeling performance. In this study, we adopted the computational design science paradigm to develop a novel information technology artifact, the deep transfer learning exploit labeler (DTL-EL). DTL-EL incorporates a pre-initialization design, multi-layer deep transfer learning (DTL), and a self-attention mechanism to automatically label exploits in hacker forums. We rigorously evaluated the proposed DTL-EL against state-of-the-art non-DTL benchmark methods based in classical machine learning and deep learning. Results suggest that the proposed DTL-EL significantly outperforms benchmark methods based on accuracy, precision, recall, and F1-score. Our proposed DTL-EL framework provides important practical implications for key stakeholders such as cybersecurity managers, analysts, and educators.

Automated Analysis of Changes in Privacy Policies: A Structured Self-Attentive Sentence Embedding Approach

The increasing societal concern for consumer information privacy has led to the enforcement of privacy regulations worldwide. In an effort to adhere to privacy regulations such as General Data Protection Regulation (GDPR), many companies’ privacy policies have become increasingly lengthy and complex. In this study, we adopted the computational design science paradigm to design a novel privacy policy evolution analytics framework to help identify how companies change and present their privacy policies based on privacy regulations. The framework includes a Self-Attentive Annotation System (SAAS) that automatically annotates paragraph-length segments in privacy policies to help stakeholders identify data practices of interest for further investigation. We rigorously evaluated SAAS against state-of-the-art Machine Learning (ML) and Deep Learning (DL)-based methods on a well-established privacy policy dataset, OPP-115. SAAS outperformed conventional ML and DL models in terms of F1-score by statistically significant margins. We demonstrate the proposed framework’s practical utility with an in-depth case study of GDPR’s impact on Amazon’s privacy policies. The case study results indicate that Amazon’s post-GDPR privacy policy potentially violates a fundamental principle of GDPR by causing consumers to exert more effort to find information about first-party data collection. Given the increasing importance of consumer information privacy, the proposed framework has important implications for regulators and companies. We discuss several design principles followed by the SAAS that can help guide future design science-based e-commerce, health, and privacy research.

To what extent can smart contracts replace traditional contracts in construction project?

PurposeThe smart contract provides an opportunity to improve existing contract management practices in the construction projects by replacing traditional contracts. However, translating the contracts into computer languages is considered a major challenge which has not been investigated. Thus, it is necessary to: (1) identify the obstructing clauses in real-world contracts; and (2) analyze the replacement's technical and economic feasibility. This paper aims to discuss the aforementioned objectives.Design/methodology/approachThis study identified the flexibility clauses of traditional contracts and their corresponding functions through inductive content analysis with representative standard contracts as materials. Through a speculative analysis in accordance to design science paradigm and new institutional economics, the economic and technical feasibility of existing approaches, including enumeration method, fuzzy algorithm, rough sets theory, machine learning and artificial intelligence, to transform respective clauses (functions) into executable codes are analyzed.FindingsThe clauses of semantic flexibility and structural flexibility are identified from the contracts. The transformation of semantic flexibility is economically and/or technically infeasible with existing methods and materials. But with more data as materials and methods of rough sets or machine learning, the transformation can be feasible. The transformation of structural flexibility is technically possible however economically unacceptable.Practical implicationsGiven smart contracts' inability to provide the required flexibility for construction projects, smart contracts will be more effective in less relational contracts. For construction contracts, the combination of smart contracts and traditional contracts is recommended. In the long run, with the sharing or trading of data in the industry level and the integration of machine learning or artificial intelligence reducing relevant costs, the automation of contract management can be achieved.Originality/valueThis study contributes to the understanding of the smart contract's limitations in industry scenarios and its role in construction project management.

Evaluation in design science: A framework to support project studies in the context of University Research Centres

This paper proposes a discussion of the evaluation of an artefact developed under the Design Science paradigm using the Delphi method. It evaluates the Ecosystem framework of University Research Centres in Project Studies, considering a set of criteria pre-established in the literature. The Delphi method is an evaluation implemented in an electronic platform involving twenty-one participants, among whom were academics, practitioners, and PhD candidates in the field of project management. It reached consensus and stability in two rounds: the results indicate a consensus among the participants in the applicability, novelty, simplicity, completeness, fidelity to modelled phenomena, consistency and internal coherence, scalability, flexibility, interest, elegance, and reusability criteria. Usability was the only criterion that did not attain the predefined percentage of consensus among the participants (70%). Given the framework’s characteristics, Delphi participants indicated the need to produce complementary guidelines for its implementation.

A Novel Mobile App for Personalized Dietary Advice Leveraging Persuasive Technology, Computer Vision, and Cloud Computing: Development and Usability Study.

The Australian Dietary Guidelines (ADG) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADG is poor in Australia. Given that following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases, there is an urgent need to develop novel technologies for individuals to improve their adherence to the ADG. This study describes the development process and design of a prototype mobile app for personalized dietary advice based on the ADG for adults in Australia, with the aim of exploring the usability of the prototype. The goal of the prototype was to provide personalized, evidence-based support for self-managing food choices in real time. The guidelines of the design science paradigm were applied to guide the design, development, and evaluation of a progressive web app using Amazon Web Services Elastic Compute Cloud services via iterations. The food layer of the Nutrition Care Process, the strategies of cognitive behavioral theory, and the ADG were translated into prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior changes. A cross-modal image-to-recipe retrieval model under an Apache 2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semistructured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (N=15). The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goal setting for food choices, and the provision of recipe ideas and information on the ADG. The overall prototype quality score was "acceptable," with a median of 3.46 (IQR 2.78-3.81) out of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (IQR 2.75-4.08) out of 5 points. In-depth interviews identified the use of gamification for tracking food choices and innovation in the image-based dietary assessment as the main drivers of the positive user experience of using the prototype. A novel evidence-based prototype mobile app was successfully developed by leveraging a cross-disciplinary collaboration. A detailed description of the development process and design of the prototype enhances its transparency and provides detailed insights into its creation. This study provides a valuable example of the development of a novel, evidence-based app for personalized dietary advice on food choices using recent advancements in computer vision. A revised version of this prototype is currently under development.

The OpenESEA Modeling Language and Tool for Ethical, Social, and Environmental Accounting

Assessing business operations’ ethical, social, and environmental impacts is a key practice for establishing sustainable development. There is a multitude of methods that describes how to perform such assessments. Often these methods are supported by an ICT tool. In most cases, the tools are developed to support a single method only and do not allow any tailoring. Therefore, they are rigid and inflexible. In this article, we present a novel model-driven approach for alleviating managerial issues that arise as a consequence of the complex landscape of ethical, social, and environmental accounting methods and tools. We have developed an open-source, model-driven tool, called openESEA. OpenESEA parses and interprets textual models, that are specified according to a domain-specific language (DSL). We have performed another iteration of the DSL engineering process, which is in line with the design science paradigm. We have validated the new DSL version by means of a user study. As a result, we present a new version of the openESEA modeling language and interpreter. The results of the user study with regards to performance, perceived usefulness, and perceived ease of use of modeling language are encouraging and provide us with a basis to continue developing new versions with more functionalities. The contributions of this work include a new version of the modeling language, a new version of the interpreter, knowledge surrounding the development of these artifacts, and a protocol for evaluating the quality of textual DSLs. The modeling language and interpreter are relevant for sustainability practitioners and consultants since our tool support has the potential to reduce redundancy in ethical, social, and environmental accounting. Our work is valuable to researchers that aim to assess and reduce the complexity of their modeling languages.

Unbox the Black-Box: Predict and Interpret YouTube Viewership Using Deep Learning

ABSTRACT As video-sharing sites emerge as a critical part of the social media landscape, video viewership prediction becomes essential for content creators and businesses to optimize influence and marketing outreach with minimum budgets. Although deep learning champions viewership prediction, it lacks interpretability, which is required by regulators and is fundamental to the prioritization of the video production process and promoting trust in algorithms. Existing interpretable predictive models face the challenges of imprecise interpretation and negligence of unstructured data. Following the design-science paradigm, we propose a novel Precise Wide-and-Deep Learning (PrecWD) to accurately predict viewership with unstructured video data and well-established features while precisely interpreting feature effects. PrecWD’s prediction outperforms benchmarks in two case studies and achieves superior interpretability in two user studies. We contribute to IS knowledge base by enabling precise interpretability in video-based predictive analytics and contribute nascent design theory with generalizable model design principles. Our system is deployable to improve video-based social media presence.

Motion Sensor–Based Fall Prevention for Senior Care: A Hidden Markov Model with Generative Adversarial Network Approach

Whereas modern medicine has enabled humans to live longer and more robust lives, recent years have seen a significant increase in chronic care costs. The prevention of threats to mobility is critical for chronic disease management. Researchers and physicians often analyze data from wearable motion sensor–based information systems (IS) to prevent falls. However, prior studies on fall prevention often achieve suboptimal performance because of their limited capacities in modeling data distributions. In this study, we adopt the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states and a logistic regression that utilizes the snippet states and determines whether and when to trigger protective devices to prevent fall injuries. We evaluate the proposed framework against prevailing fall-prevention models and the HMM-GAN component against state-of-the-art sensor analytics models on large-scale data sets. Through an in-depth case study, we demonstrate how the proposed framework can lead to significantly reduced potentially catastrophic falls. Besides practical health information technology contributions, HMM-GAN offers methodological contributions to the IS knowledge base for scholars designing novel IT artifacts for healthcare applications.

Designing for Trust in Blockchain Platforms

Trust is a crucial component for successful transactions regardless of whether they are executed in physical or virtual spaces. Blockchain technology is often discussed in the context of trust and referred to as a trust-free, trustless, or trustworthy technology. However, the question of how the trustworthiness of blockchain platforms should be demonstrated and proven to end users still remains open. While there may be some genuine trust in the blockchain technology itself, on an application level trust in an IT artifact needs to be established. In this article, we examine how trust-supporting design elements may be implemented to foster an end user's trust in a blockchain platform. We follow the design science paradigm and suggest a practically useful set of design elements that can help designers of blockchain platforms to build more trustworthy systems.

Discovering Emerging Threats in the Hacker Community: A Nonparametric Emerging Topic Detection Framework

The prevalence and rapid growth of cybercrime are largely attributed to hacker communities on the dark web, where cybercriminals extensively exchange hacking resources, share hacking knowledge, and organize cyberattacks. Such streams of hacker-generated content constitute an invaluable data source for developing threat intelligence that can inform organizations of cybersecurity risks and facilitate proactive cyber defense. Drawing upon the design science paradigm, we propose a novel nonparametric emerging topic detection (NPETD) framework for detecting emerging topics in streams of hacker-generated content. Our framework extends the state-of-the-art nonparametric topic model to inductively model topics without having to specify the number of topics a priori. Moreover, our framework features an efficient algorithm to jointly infer topics and detect topic emergence. We conducted experiments to rigorously evaluate the effectiveness and efficiency of our framework in comparison with the state-of-the-art baseline methods. Our framework outperformed the baseline methods in detecting the listings of emerging threats in darknet marketplaces on recall, F-measure, topic coherence, and processor time. The practical utility of our framework is further demonstrated in a major hacker forum, where we identified several notable emerging topics with important implications for victim companies and law enforcement. The proposed framework contributes to cybersecurity, topic detection and tracking, and design science.

Applicability Of Design Science Research For Biomedical Engineering Research

The design-science paradigm aims to push the limits of human and organisational capabilities by generating new and inventive artefacts. The design science research framework includes engineering that applies science through the design process to solve challenges. The design process looks for a solution to meet the environment's needs. On the other hand, a research study results from a collaboration between design and knowledge base to answer a research problem. 
 The three principal cycles in design science research are the relevance cycle between the environment and the design phase, the design cycle within the design phase, and the rigour cycle between the design phase and the knowledge base. While it is most well-known in the engineering and computer science disciplines, it is also applied in biomedical engineering and bioinformatics. It is relevant in biomedical engineering because of the connection between research outputs and the issue of scientific rigour. Three cases are presented in this article to depict design science study in biomedical and bioinformatics research. For each case discussed, machine learning has a big chance accompanied by unique challenges. In conclusion, the design science paradigm can also be used in biomedical engineering research to ensure that knowledge is relevant and appropriately included in the research process.

Human-machine hybrid prediction market: A promising sales forecasting solution for E-commerce enterprises

Integrating machine models and humans in the prediction market could generate hybrid intelligence for sales forecasting which is of great significance to e-commerce enterprises. Following the design science paradigm, we propose a framework of human–machine hybrid prediction market and evaluate its effectiveness with economic analytical models and numerical simulations. We obtain four interesting design guidelines by modeling and analyzing three kinds of hybrid markets: the collaboration market, competition market, and co-competition market. First, machines’ collaboration behavior, i.e., prediction result sharing, does not always benefit the prediction market. Second, compared with the collaboration pattern, introducing machines as competitors with humans is a better approach. Third, co-competition between machines and humans will bring positive and negative effects simultaneously, and this kind of interaction will contribute to the prediction performance when positive effects dominate negative effects. Fourth, when human traders put correct trust on machine models, the co-competition market performs best.

Digital mock-ups as support tools for preventing risks related to energy sources in the operation stage of industrial facilities through design

Building information modelling (BIM) and product lifecycle management (PLM) technologies provide automatic model checking (AMC) tools that can be used for the prevention of occupational health and safety (OHS) risks through design (PtD). Considering that the risks related to energy sources during the operation stage of industrial facilities can be major, our objective is to propose a PtD approach for this type of risk that uses AMC tools. For this purpose, our methodology is based on the information systems design-science paradigm. Considering that both BIM and PLM mock-ups can be used to design industrial facilities and that Catia V5 is one of the main software programs used to design industrial equipment and facilities, our methodology specifically includes a comparative literature review of the uses of AMC tools to check BIM and PLM mock-ups and a comparative study of the OHS risk prevention capabilities of the AMC tools available in Catia V5. Therefore, the PtD approach that we propose is specific to Catia V5. It consists of: 1) extracting rules related to hazardous energy control from regulatory requirements and classifying them according to their automation potential, 2) expressing the rules in a form that is compatible with the RASE method, 3) interpreting the rules using the RASE method, and 4) using a macro script to automatically check the compliance of the digital mock-ups. The proposed approach's contribution is that it makes it possible to support facility designers in automatically identifying hazardous energy sources in systems. In future studies, we intend to couple this PtD approach with methods that integrate dynamic system behavior to assess the level of risk corresponding to the hazardous sources identified.

Wearable Sensor-Based Chronic Condition Severity Assessment: An Adversarial Attention-Based Deep Multisource Multitask Learning Approach

Advancing the quality of healthcare for senior citizens with chronic conditions is of great social relevance. To better manage chronic conditions, objective, convenient, and inexpensive wearable sensor- based information systems (IS) have been increasingly used by researchers and practitioners. However, existing models often focus on a single aspect of chronic conditions and are often “black boxes” with limited interpretability. In this research, we adopt the computational design science paradigm and propose a novel adversarial attention-based deep multisource multitask learning (AADMML) framework. Drawing upon deep learning, multitask learning, multisource learning, attention mechanism, and adversarial learning, AADMML addresses limitations with existing wearable sensor-based chronic condition severity assessment methods. Choosing Parkinson’s disease (PD) as our test case because of its prevalence and societal significance, we conduct benchmark experiments to evaluate AADMML against state-of-the-art models on a large-scale dataset containing thousands of instances. We present three case studies to demonstrate the practical utility and economic benefits of AADMML and by applying it to detect early-stage PD. We discuss how our work is related to the IS knowledge base and its practical implications. This work can contribute to improved life quality for senior citizens and advance IS research in mobile health analytics.

Designing a Secure Blockchain-Based Supply Chain Management Framework

ABSTRACT Supply chain management (SCM) faces a critical security issue because of the asymmetry of information delivered to various parties in the ecosystem and the lack of corresponding supervision. In response, we propose the use of blockchain technology to address the SCM security issues and put forward a blockchain-based SCM framework. We apply design science paradigm to guide the blockchain-based SCM framework development and implementation of a proof-of-concept prototype. We use Hyperledger Fabric and Composer to develop the prototype artifact. Performance evaluation results issued from Hyperledger Caliper prove the superiority and robustness of the proposed blockchain-based framework in terms of security and efficiency requirements, and performance metrics including throughput and latency. Also, the evaluation results show that the IT artifact is stable, and the high stability can reduce the risks of system vulnerabilities and breakdown.

Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model

Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel deep learning (DL)- based exploit-vulnerability attention deep structured semantic model (EVA-DSSM) that includes bidirectional processing and attention mechanisms to automatically link exploits from the Dark Web to vulnerabilities. We also devised a novel device vulnerability severity metric (DVSM) that incorporates the exploit post date and vulnerability severity to help cybersecurity professionals with their device prioritization and risk management efforts. We rigorously evaluated the EVA-DSSM against state-of-theart non-DL and DL-based methods for short text matching on 52,590 exploit-vulnerability linkages across four testbeds: web application, remote, local, and denial of service. Results of these evaluations indicate that the proposed EVA-DSSM achieves precision at 1 scores 20% - 41% higher than non-DL approaches and 4% - 10% higher than DL-based approaches. We demonstrated the EVA-DSSM’s and DVSM’s practical utility with two CTI case studies: openly accessible systems in the top eight U.S. hospitals and over 20,000 Supervisory Control and Data Acquisition (SCADA) systems worldwide. A complementary user evaluation of the case study results indicated that 45 cybersecurity professionals found the EVADSSM and DVSM results more useful for exploit-vulnerability linking and risk prioritization activities than those produced by prevailing approaches. Given the rising cost of cyberattacks, the EVA-DSSM and DVSM have important implications for analysts in security operations centers, incident response teams, and cybersecurity vendors.

Cross-Lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning

International dark web platforms operating within multiple geopolitical regions and languages host a myriad of hacker assets such as malware, hacking tools, hacking tutorials, and malicious source code. Cybersecurity analytics organizations employ machine learning models trained on human-labeled data to automatically detect these assets and bolster their situational awareness. However, the lack of human-labeled training data is prohibitive when analyzing foreign-language dark web content. In this research note, we adopt the computational design science paradigm to develop a novel IT artifact for cross-lingual hacker asset detection (CLHAD). CLHAD automatically leverages the knowledge learned from English content to detect hacker assets in non-English dark web platforms. CLHAD encompasses a novel Adversarial deep representation learning (ADREL) method, which generates multilingual text representations using generative adversarial networks (GANs). Drawing upon the state of the art in cross-lingual knowledge transfer, ADREL is a novel approach to automatically extract transferable text representations and facilitate the analysis of multilingual content. We evaluate CLHAD on Russian, French, and Italian dark web platforms and demonstrate its practical utility in hacker asset profiling, and conduct a proof-of-concept case study. Our analysis suggests that cybersecurity managers may benefit more from focusing on Russian to identify sophisticated hacking assets. In contrast, financial hacker assets are scattered among several dominant dark web languages. Managerial insights for security managers are discussed at operational and strategic levels.

Scenario modeling for government big data governance decision-making: Chinese experience with public safety services

In the public safety service context, government big data governance (GBDG) is a challenging decision-making problem that encompasses uncertainties in the arenas of big data and its complex links. Modeling and collaborating the key scenario information required for GBDG decision-making can minimize system uncertainties. However, existing scenario-building methods are limited by their rigidity as they are employed in various application contexts and the associated high costs of modeling. In this paper, using a design science paradigm, a model-driven scenario modeling approach is proposed to achieve flexible scenario modeling for various applications through the transfer of generic domain knowledge. The key component of the proposed approach is a scenario meta-model that is built from existing literatures and practices by integrating qualitative, quantitative, and meta-modeling analysis. An instantiation mechanism of the scenario meta-model is also proposed to generate customized scenarios under Antecedent-Behavior-Consequence (ABC) theory. Two real-world safety service cases in Wuhan, China were evaluated to find that the proposed approach reduces GBDG decision-making uncertainties significantly by providing key information for GBDG problem identification, solution design, and solution value perception. This scenario-building approach can be further used to develop other GBDG systems for public safety services with reduced uncertainties and complete decision-making functions.