Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Data Validation01:15

Data Validation

400
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
400
Data Validation01:03

Data Validation

6.1K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
6.1K
Reliability and Validity01:29

Reliability and Validity

13.4K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
13.4K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

176
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
176
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

3.4K
Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
3.4K
Nominal Level of Measurement00:56

Nominal Level of Measurement

33.9K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
33.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A roadmap for managing an ageing workforce in the manufacturing sector: An Italian case study.

Open research Europe·2025
Same author

A new roadmap for an age-inclusive workforce management practice and an international policies comparison.

Open research Europe·2024
Same author

Business continuity, disaster readiness and performance in COVID-19 outbreak aftermath: A survey.

IFAC-PapersOnLine·2024
Same author

Predicting the effects of supply chain resilience and robustness on COVID-19 impacts and performance: Empirical investigation through resources orchestration perspective.

Journal of business research·2023
Same author

Can supply chain risk management practices mitigate the disruption impacts on supply chains' resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era.

International journal of production economics·2022
Same author

Blackout and supply chains: Cross-structural ripple effect, performance, resilience and viability impact analysis.

Annals of operations research·2022

Related Experiment Video

Updated: Nov 13, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.1K

Supply chain viability: conceptualization, measurement, and nomological validation.

Salomée Ruel1, Jamal El Baz2, Dmitry Ivanov3

  • 1MOSI department - CSR excellence center, KEDGE Business School, Domaine de Luminy, Rue Antoine Bourdelle, 13009 Marseille, France.

Annals of Operations Research
|March 15, 2021
PubMed
Summary

This study introduces Supply Chain Viability (SCV) as a distinct construct, offering a validated scale to measure its multidimensional nature. It provides guidance for enhancing SCV amidst disruptions like the COVID-19 pandemic.

Keywords:
COVID-19 pandemicMeasurementScale developmentSecond-order constructSupply chain viability

More Related Videos

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

416
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.3K

Related Experiment Videos

Last Updated: Nov 13, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.1K
Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

416
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

5.3K

Area of Science:

  • Operations Management
  • Supply Chain Management
  • Business Strategy

Background:

  • Supply Chain Viability (SCV) is an emerging and increasingly critical concept in operations management.
  • Existing literature lacks a validated measurement scale for SCV, hindering its empirical investigation.
  • The COVID-19 pandemic highlighted the need for robust supply chain resilience and adaptability.

Purpose of the Study:

  • To conceptualize, develop, and validate a measurement scale for Supply Chain Viability (SCV).
  • To establish SCV as a novel and distinct construct within operations management.
  • To offer practical guidance for improving SCV in the face of disruptions.

Main Methods:

  • Scale development through exploratory and confirmatory factor analyses using data from 558 respondents across three samples.
  • Content validation and item measure development.
  • Nomological model testing to assess nomological validity.

Main Results:

  • SCV is conceptualized and validated as a hierarchical and multidimensional construct.
  • Key dimensions of SCV include organizational structures, resources, dynamic design capabilities, and operational aspects.
  • Dynamic reconfiguration of supply chain structures is central to SCV for long-term survival.

Conclusions:

  • The study provides the first validated scale for measuring Supply Chain Viability (SCV).
  • SCV is characterized by adaptive reconfiguration of supply chain structures.
  • Findings offer actionable insights for practitioners to enhance SCV, particularly during crises.