Distinguishing Standards and Regulation for Innovation Research

Tineke M. Egyedi; Arjan Widlak; J. Roland Ortt

doi:10.4018/ijsr.2018070101

Distinguishing Standards and Regulation for Innovation Research

International Journal of Standardization Research ◽

10.4018/ijsr.2018070101 ◽

2018 ◽

Vol 16 (2) ◽

pp. 1-21

Author(s):

Tineke M. Egyedi ◽

Arjan Widlak ◽

J. Roland Ortt

Keyword(s):

Indirect Effects ◽

Black Box ◽

Careful Consideration ◽

Impact Studies ◽

Innovation Research ◽

Separate Treatment ◽

Special Instance ◽

Innovation Impact

Certain influential innovation impact studies do not sharply distinguish standards from regulation. Is differentiation needed? In what way do they differ in how they work and work out? This article applies and extends a framework of regulatory modalities to open up the black box of direct innovation effects. It includes standards as a separate regulatory modality following careful consideration of alternatives, i.e., accommodating them as a special instance or as a hybrid of law, norm, market and architecture. The authors capture the essential differences between standards and law. They reconcile Lessig's emphasis on constraints with findings of enabling and constraining effects in innovation research by differentiating direct inherently constraining effects of regulatory modalities and modality-specific direct generic effects - as opposed to indirect effects. They conclude that standards and law merit separate treatment in innovation research, and recommend complementary frameworks to uncover unaddressed issues.

Download Full-text

Exploring the Black Box between Pay Dispersion and Performance: A Conditional Indirect Effects Model

Academy of Management Proceedings ◽

10.5465/ambpp.2016.52 ◽

2016 ◽

Vol 2016 (1) ◽

pp. 16425

Author(s):

Matthew B. Perrigino ◽

Benjamin B. Dunford ◽

Wayne Boss

Keyword(s):

Indirect Effects ◽

Black Box ◽

Pay Dispersion ◽

And Performance ◽

Conditional Indirect Effects

Download Full-text

Shining a Light Inside the “Black Box” of NIH Application Submission and Review

Innovation in Aging ◽

10.1093/geroni/igab046.2810 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 765-765

Author(s):

Elia Ortenberg ◽

Shalanda Bynum

Keyword(s):

Peer Review ◽

Black Box ◽

National Institutes Of Health ◽

Grant Application ◽

Scientific Review ◽

Training Opportunity ◽

Business Innovation ◽

Innovation Research ◽

Grant Applications ◽

Potential Impact

Abstract What happens to applications after they are submitted to the National Institutes of Health, and how can you better prepare yourself and your application for the process of peer review? The Center for Scientific Review (CSR) works closely with the 24 funding institutes and centers at the National Institutes of Health that provide funding support for projects of high scientific merit and high potential impact. CSR conducts the first level of review for the majority of grant applications submitted to the NIH, which includes 90% of R01s, 85% of Fellowships, and 95% of Small Business Innovation Research (SBIR) applications as well as many other research and training opportunity activities. In this capacity, CSR helps to identify the most meritorious projects, cutting-edge research, and future scientists who will advance the mission of the NIH: to enhance health, lengthen life, and reduce illness and disability. The purpose of this project is to provide an overview of 1) what happens to NIH applications before, during, and after peer review at CSR; 2) a summary of new and current peer review policies and practices that impact investigators and their submitted applications; and 3) strategies for developing a strong NIH grant application. Peer review is the cornerstone of the NIH grant supporting process, and an insider’s view can shine a light inside the “Black Box” of how the most meritorious projects are identified.

Download Full-text

A Lesson on Operationalizing Machine Learning for Predictive Maintenance of Gas Turbines

10.1115/gt2021-59249 ◽

2021 ◽

Author(s):

Paolo Pileggi ◽

Elena Lazovik ◽

Ron Snijders ◽

Lars-Uno Axelsson ◽

Sietse Drost ◽

...

Keyword(s):

Machine Learning ◽

Gas Turbines ◽

Service Providers ◽

Black Box ◽

Careful Consideration ◽

Knowledge Systems ◽

Predictive Maintenance ◽

Data Driven ◽

Use Case ◽

Digital Twin

Abstract OEMs, service providers and end-users are moving from preventative to predictive maintenance to minimize the risk of unwanted power plant shut-downs and to maximize profitability. Digital Twin and Machine Learning (ML) are important techniques in this transformation as it complements and improves the traditional expert-based knowledge systems. There is a continued trend to use data-driven, so-called black-box, ML techniques as an improvement over traditional statistical approaches. However, these ML approaches suffer from low interpretability or explainability, making it hard to trust how or why a certain anomaly in the system is detected, limiting the trust in the prediction and making it much less likely to identify the real original cause of the problem. In this paper, we present our lesson learnt from operationalizing ML in a real-world use case that studied data from the 1.85 MWe OPRA OP16 all radial single-shaft gas turbine. We comment on the unforeseen obstacles we uncovered during our ML anomaly detection application and juxtapose them with the high potential value that our novel ML applications and explanation method can provide. ML may be enticing for the predictive maintenance of gas turbines but our lesson makes it clear that operationalizing ML goes beyond merely algorithm specifics. In line with the nature of the Digital Twin, it requires careful consideration of the specialized IT system supporting the algorithm, and the specific process it supports and in which it is deployed.

Download Full-text