Related Experiment Video
Updated: Feb 19, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.7K
Evaluating firms' R&D performance using best worst method
1Science Based Business, Faculty of Science, Leiden University, The Netherlands.
Evaluation and Program Planning
|November 2, 2017
Summary
Measuring research and development (R&D) performance accurately is crucial for business growth. This study introduces a new method to weigh R&D measures, leading to better performance insights and strategies for high-tech firms.
Area of Science:
- Business and Management
- Innovation Studies
- Decision Sciences
Background:
- Research and Development (R&D) is vital for firm productivity, growth, and competitive advantage.
- Existing R&D performance measurement methods often oversimplify by assigning equal importance to all measures, potentially leading to flawed strategies.
- Accurate R&D performance evaluation is essential for effective strategic decision-making.
Purpose of the Study:
- To measure R&D performance by considering the varying importance of different R&D measures.
- To apply a multi-criteria decision-making method, the Best Worst Method (BWM), for weighting R&D measures.
- To analyze the R&D performance of 50 high-tech Small and Medium-sized Enterprises (SMEs) in the Netherlands.
Main Methods:
- Utilized the Best Worst Method (BWM) to determine the weights (importance) of various R&D measures.
- Conducted a survey among 50 high-tech SMEs in the Netherlands to gather performance data.
- Collected data from R&D experts to inform the weighting process.
Main Results:
- Assigning different weights to R&D measures significantly alters the performance ranking of firms compared to using a simple average.
- The study identified distinct R&D performance rankings based on the weighted importance of measures.
- Results highlight the impact of differential weighting on R&D performance evaluation.
Conclusions:
- Standard R&D performance measurement can be misleading due to uniform weighting of measures.
- The BWM provides a more nuanced approach to R&D performance assessment by incorporating measure importance.
- Findings enable R&D managers to develop more effective strategies by understanding the relative significance of different R&D measures.
Related Concept Videos
01:16Response Surface Methodology
688
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
The process of RSM involves several key steps:
688
01:21Friedman Two-way Analysis of Variance by Ranks
517
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
517
01:09Decision Making: P-value Method
7.0K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
7.0K
01:19Testing a Claim about Standard Deviation
3.0K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
3.0K
01:02Ranks
522
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
522
01:19Routh-Hurwitz Criterion II
1.1K
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
1.1K