An Intelligent Mechanism to Automatically Discover Emerging Technology Trends: Exploring Regulatory Technology

2022 ◽  
Vol 13 (2) ◽  
pp. 1-29
Author(s):  
Shi Ming Huang ◽  
David C. Yen ◽  
Ting Jyun Yan ◽  
Yi Ting Yang

Technology trend analysis uses data relevant to historical performance and extrapolates it to estimate and assess the future potential of technology. Such analysis is used to analyze emerging technologies or predict the growing markets that influence the resulting social or economic development to assist in effective decision-making. Traditional trend analysis methods are time-consuming and require considerable labor. Moreover, the implemented processes may largely rely on the specific knowledge of the domain experts. With the advancement in the areas of science and technology, emerging cross-domain trends have received growing attention for its considerable influence on society and the economy. Consequently, emerging cross-domain predictions that combine or complement various technologies or integrate with diverse disciplines may be more critical than other tools and applications in the same domain. This study uses a design science research methodology, a text mining technique, and social network analysis (SNA) to analyze the development trends concerning the presentation of the product or service information on a company's website. This study applies regulatory technology (RegTech) as a case to analyze and justify the emerging cross-disciplinary trend. Furthermore, an experimental study is conducted using the Google search engine to verify and validate the proposed research mechanism at the end of this study. The study results reveal that, compared with Google Trends and Google Correlate, the research mechanism proposed in this study is more illustrative, feasible, and promising because it reduces noise and avoids the additional time and effort required to perform a further in-depth exploration to obtain the information.

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Andrea Herrera ◽  
Paola Lara ◽  
Mario Sánchez ◽  
Jorge Villalobos

PurposeThis paper proposes a conceptualization of the e-waste domain, formalized through a metamodel, to express complex e-waste realities in a simple manner. This also enables the transition from a structural model to a behavioral model to implement analysis techniques.Design/methodology/approachThe methodology used is design science research (DSR), a problem-solving paradigm which seeks to construct a working artifact and prove its relevance. The artifact, a metamodel for the e-waste domain, was constructed through an iterative manner and later analyzed to conclude its theoretical relevance and contributions in this domain. As part of the approach, the authors used supplementary techniques such as systematic literature review (SLR), conceptual modeling (CM) and system dynamics (SD).FindingsThe application in the e-waste domain of CM techniques such as metamodeling, model-to-model transformation and simulation is valuable for supporting decision-making, especially when combined with SD. The approach presented in this paper, the conceptual tools and different simulation techniques could also be applied in other complex domains to obtain similar results.Practical implicationsThe modeling method to apply simulation techniques is targeted toward the e-waste domain experts to understand, design, implement, measure and improve strategies and public policies.Originality/valueThe use of CM techniques to model and analyze structural and behavioral e-waste scenarios.


2021 ◽  
Vol 8 ◽  
Author(s):  
Mohammed Odeh ◽  
Faten F. Kharbat ◽  
Rana Yousef ◽  
Yousra Odeh ◽  
Dina Tbaishat ◽  
...  

Background: Few ontological attempts have been reported for conceptualizing the bioethics domain. In addition to limited scope representativeness and lack of robust methodological approaches in driving research design and evaluation of bioethics ontologies, no bioethics ontologies exist for pandemics and COVID-19. This research attempted to investigate whether studying the bioethics research literature, from the inception of bioethics research publications, facilitates developing highly agile, and representative computational bioethics ontology as a foundation for the automatic governance of bioethics processes in general and the COVID-19 pandemic in particular.Research Design: The iOntoBioethics agile research framework adopted the Design Science Research Methodology. Using systematic literature mapping, the search space resulted in 26,170 Scopus indexed bioethics articles, published since 1971. iOntoBioethics underwent two distinctive stages: (1) Manually Constructing Bioethics (MCB) ontology from selected bioethics sources, and (2) Automatically generating bioethics ontological topic models with all 26,170 sources and using special-purpose developed Text Mining and Machine-Learning (TM&ML) engine. Bioethics domain experts validated these ontologies, and further extended to construct and validate the Bioethics COVID-19 Pandemic Ontology.Results: Cross-validation of the MCB and TM&ML bioethics ontologies confirmed that the latter provided higher-level abstraction for bioethics entities with well-structured bioethics ontology class hierarchy compared to the MCB ontology. However, both bioethics ontologies were found to complement each other forming a highly comprehensive Bioethics Ontology with around 700 concepts and associations COVID-19 inclusive.Conclusion:The iOntoBioethics framework yielded the first agile, semi-automatically generated, literature-based, and domain experts validated General Bioethics and Bioethics Pandemic Ontologies Operable in COVID-19 context with readiness for automatic governance of bioethics processes. These ontologies will be regularly and semi-automatically enriched as iOntoBioethics is proposed as an open platform for scientific and healthcare communities, in their infancy COVID-19 learning stage. iOntoBioethics not only it contributes to better understanding of bioethics processes, but also serves as a bridge linking these processes to healthcare systems. Such big data analytics platform has the potential to automatically inform bioethics governance adherence given the plethora of developing bioethics and COVID-19 pandemic knowledge. Finally, iOntoBioethics contributes toward setting the first building block for forming the field of “Bioethics Informatics”.


Cancers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 24
Author(s):  
Gerald McGwin

N-nitrosodimethylamine (NDMA) is a carcinogen in experimental animals. It has been classified a probable human carcinogen and has been found in ranitidine. This study sought to evaluate the association between ranitidine use and cancer of the gastrointestinal system. Events reported to the FDA Adverse Events Reporting System that were associated with the use of proton pump inhibitors (PPIs) and H2 antagonists were selected. Proportionate reporting ratios (PRRs) and associated 95% confidence intervals (CIs) were calculated to compare the proportion of all reported adverse events that were for gastrointestinal system cancers among adverse event reports for ranitidine to adverse event reports for other H2 antagonists. The proportion of adverse events for any gastrointestinal system cancer relative to all other events was elevated for ranitidine compared to PPIs and other H2 antagonists (PRR 3.66, 95% CI 3.19–4.20). Elevated and significant PRRs were observed for pharyngeal (PRR 9.24), esophageal (PRR 3.56), stomach (PRR 1.48), colorectal (PRR 16.31), liver (PRR 2.64), and pancreatic (PRR 2.18) cancers. The PRRs for anal (PRR 4.62) and gallbladder (PRR 4.62) cancer were also elevated though not statistically significant. In conjunction with a large body of epidemiologic and human and animal basic science research, the study results support the hypothesis that NDMA-contaminated ranitidine increases the risk of cancer and supports the withdrawal of these medications from the market.


Logistics ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 37
Author(s):  
Serkan Alacam ◽  
Asli Sencer

In the global trucking industry, vertical collaboration between shippers and carriers is attained by intermediaries, called brokers. Brokers organize carriers for a shipper in accordance with its quality and price requirements, and support carriers to collaborate horizontally by sharing a large distribution order from a shipper. Brokers also act as trustees, preventing the passing of private information of any party to the others. Despite these benefits, intermediaries in the trucking industry are involved in several sustainability problems, including high costs, high levels of carbon emissions, high percentages of empty miles, low-capacity utilizations, and driver shortages. Several studies have acknowledged the importance of improving collaboration to address these problems. Obviously, the major concern of brokers is not collaboration, but rather to optimize their own gains. This paper investigates the potential of blockchain technology to improve collaboration in the trucking industry, by eliminating brokers while preserving their responsibilities as organizers and trustees. This paper extends the transportation control tower concept from the logistics literature, and presents a system architecture for its implementation through smart contracts on a blockchain network. In the proposed system, the scalability and privacy of trucking operations are ensured through integration with privacy-preserving off-chain computation and storage solutions (running outside of the blockchain). The potential of this design artifact for fostering collaboration in the trucking industry was evaluated by both blockchain technology experts and trucking industry professionals.


2021 ◽  
Vol 13 (13) ◽  
pp. 7070
Author(s):  
Eleonora Di Di Matteo ◽  
Paolo Roma ◽  
Santo Zafonte ◽  
Umberto Panniello ◽  
Lorenzo Abbate

Decision support systems (DSSs) have been traditionally identified as useful information technology tools in a variety of fields, including the context of cultural heritage. However, to the best of our knowledge, no prior study has developed a DSS framework that incorporates all the main decision areas simultaneously in the context of cultural heritage. We fill this gap by focusing on design-science research and specifically by developing a DSS framework whose features support all the main decision areas for the sustainable management of cultural assets in a comprehensive manner. The main decision-making areas considered in our study encompass demand management, segmentation and communication, pricing, space management, and services management. For these areas, we select appropriate decision-making supporting techniques and data management solutions. The development of our framework, in the form of a web-based system, results in an architectural solution that is able to satisfy critical requirements such as ease of use and response time. We present an application of the innovative DSS framework to a museum and discuss the main managerial implications and future improvements.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bayu Rima Aditya ◽  
Ridi Ferdiana ◽  
Sri Suning Kusumawardani

PurposeExisting literature has reported a barrier list that could affect the implementation of digital transformation in higher education, yet the research question of how to identify barriers remained unanswered. Thus, this study intended to address this gap.Design/methodology/approachThe research design adopted a mixed-methods approach based on the problem-centered design science research (DSR) process model for the development and evaluation of framework.FindingsThis study proposed a systematic framework of three sets of components: (1) the initial set of barriers; (2) the barrier rating scheme and (3) the barrier scoring matrix. The three-component of the framework is to identify and prioritize barriers to the successful implementation of digital transformation in higher education.Research limitations/implicationsThe evaluation of the framework was only based on an expert opinion.Practical implicationsThis study provided a direction to the policymakers for designing sensible strategies to increase the chances of a successful digital transformation in higher education.Originality/valueThis study contributes to the knowledge body by offering a more systematic understanding of barriers to digital transformation in higher education.


Author(s):  
Abdullah Albizri ◽  
Deniz Appelbaum

Although research shows that blockchain provides fairly immutable virtual provenance workflows, proof that the Blockchain accurately represents physical events lacks truly independent verification. This dilemma, the Oracle Paradox, challenges blockchain architecture and is perhaps one reason why businesses have hesitated to adopt smart contracts. Blockchain proponents claim that people can serve as trusted Oracles in a smart contract. However, auditing research shows that people are the weak link in almost every internal control application, including those pertaining to blockchain. People are susceptible to collusion, bribery, error, and fraud and these tendencies are not entirely mitigated by blockchain technologies (Balagurusamy et al. 2019; Nakamoto 2008). This research proposes a framework to mitigate the paradox of the Oracle: A Business Process Management (BPM) model of a Blockchain Smart Contract-enabled Supply Chain with IoT as the sole "third-party" Oracle participant, utilizing Design Science research.


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