Short answer
When developing new products or features, use statistical modeling techniques like PLS-SEM to understand and predict the impact of design choices on user behavior and market success.
- Field
- Innovation & Design
- Source
- Information & Management (2019)
- Method
- Methodological guidelines and best practices
- Evidence
- Strong effect
Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) can significantly improve the predictive power of models used to understand the complex relationships between various factors influencing innovation and product success. This innovation & design research insight is drawn from a 2019 study published in Information & Management. Using Methodological guidelines and best practices, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When developing new products or features, use statistical modeling techniques like PLS-SEM to understand and predict the impact of design choices on user behavior and market success.
PLS-SEM adoption enhances predictive accuracy in new product development models
Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) can significantly improve the predictive power of models used to understand the complex relationships between various factors influencing innovation and product success.
Information & Management · 2019
Key Findings
- 01PLS-SEM is a versatile method suitable for both predictive and explanatory research in IS.
- 02Clear guidelines are needed for model specification, estimation, and reporting to ensure the validity and reliability of PLS-SEM results.
- 03The method can effectively handle complex models with multiple latent variables and relationships.
Application
Design takeaway
When developing new products or features, use statistical modeling techniques like PLS-SEM to understand and predict the impact of design choices on user behavior and market success.
How to apply
Use PLS-SEM to model the relationships between proposed design features (independent variables) and desired user outcomes (dependent variables) in your product development process.
Project actions
- 01If your project involves testing multiple design ideas and predicting which one will be most successful, consider how you might use a statistical approach like PLS-SEM (or a simplified version) to analyze your data.
- 02Think about the 'cause and effect' relationships in your design: what design choices might lead to better user experience or higher sales?
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Provides a robust framework for analyzing complex, multi-variable relationships.
- +Suitable for predictive modeling, which is highly relevant for innovation.
- +Offers clear guidelines for application and reporting.
Limitations
Performing a full PLS-SEM analysis requires specialized software and statistical knowledge. The complexity of the models and data requirements may be beyond the scope of a typical design project.
Reliability & validity
The paper emphasizes the importance of model fit indices and reliability measures (like Cronbach's alpha and composite reliability) to ensure the validity and reliability of PLS-SEM results. Adherence to the outlined guidelines is crucial for achieving trustworthy outcomes.
Think critically
How might the principles of PLS-SEM be applied conceptually to a design problem, even without performing the full statistical analysis?
Design Principles
"Complex relationships between design variables and user outcomes can be quantitatively modeled and predicted using advanced statistical techniques."
This analytical technique allows designers and researchers to test and refine complex conceptual models, such as those predicting market adoption or user satisfaction, by identifying key drivers and their interdependencies. Understanding these relationships is crucial for making informed design decisions and optimizing product development strategies.
What This Means for Your Design
This paper explains a powerful way to use data to figure out which design ideas are most likely to work before you even build anything. It's like having a crystal ball for your product development!
How to use in your project
- 1.In your project, if you are comparing multiple design solutions or testing hypotheses about user preferences, you could mention how advanced analytical techniques like PLS-SEM are used in industry to validate such models, even if you cannot perform the full analysis yourself.
- 2.You can use the principles of model building and variable identification discussed in the paper to structure your own design thinking and hypothesis generation.
Add to My Project
Quick Cite
Paragraph starter
Advanced analytical techniques such as Partial Least Squares Structural Equation Modeling (PLS-SEM) are employed in industry to rigorously test and refine conceptual models of innovation and product success. As highlighted by Benitez et al. (2019), PLS-SEM provides a robust framework for understanding complex interdependencies between design variables and user outcomes, enabling more accurate predictions of market reception and product performance. This approach allows for data-driven validation of design hypotheses and optimization of development strategies.
Source
Information & Management
How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research
journal · 2019
View sourceQuestions About This Research
- What does the research say about pls-sem adoption enhances predictive accuracy in new product development models?
- When developing new products or features, use statistical modeling techniques like PLS-SEM to understand and predict the impact of design choices on user behavior and market success. Evidence: Information & Management (2019).
- Why does "PLS-SEM adoption enhances predictive accuracy in new product development models" matter for design?
- This analytical technique allows designers and researchers to test and refine complex conceptual models, such as those predicting market adoption or user satisfaction, by identifying key drivers and their interdependencies. Understanding these relationships is crucial for making informed design decisions and optimizing product development strategies.
- How can designers apply this research?
- When developing new products or features, use statistical modeling techniques like PLS-SEM to understand and predict the impact of design choices on user behavior and market success.
- What were the main findings?
- PLS-SEM is a versatile method suitable for both predictive and explanatory research in IS.. Clear guidelines are needed for model specification, estimation, and reporting to ensure the validity and reliability of PLS-SEM results.. The method can effectively handle complex models with multiple latent variables and relationships.
- What research method was used?
- Methodological guidelines and best practices.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2019 journal from Information & Management.
- What should I do differently in my next project?
- Use PLS-SEM to model the relationships between proposed design features (independent variables) and desired user outcomes (dependent variables) in your product development process.
- What are the limitations?
- The paper focuses on IS research; direct application to purely physical product design might require adaptation. The effectiveness of PLS-SEM depends on the quality of the data and the appropriateness of the model specification.