Short answer
Integrate big data analytics into performance measurement systems to create a dynamic feedback loop that informs and drives ongoing business model innovation for sustainability.
- Field
- Innovation & Design
- Source
- Sustainability (2019)
- Method
- Design Science Research Methodology
- Evidence
- Strong effect
Leveraging big data analytics to measure performance indicators can proactively drive continuous business model innovation for sustainability. This innovation & design research insight is drawn from a 2019 study published in Sustainability. Using Design science research methodology, researchers explored how this design variable affects real-world outcomes. The key design takeaway: Integrate big data analytics into performance measurement systems to create a dynamic feedback loop that informs and drives ongoing business model innovation for sustainability.
Big Data Analytics Accelerates Sustainable Business Model Innovation
Leveraging big data analytics to measure performance indicators can proactively drive continuous business model innovation for sustainability.
Sustainability · 2019
Key Findings
- 01The developed artifact effectively supports proactive and continuous business model innovation efforts.
- 02The artifact's reliance on big data analytics is accessible and beneficial for small businesses, democratizing innovation practices.
- 03Connecting business model innovation with performance management and big data analytics provides actionable paths for operationalization.
Application
Design takeaway
Integrate big data analytics into performance measurement systems to create a dynamic feedback loop that informs and drives ongoing business model innovation for sustainability.
How to apply
Implement a performance dashboard that pulls data from various sources (sales, customer feedback, supply chain) and uses analytical tools to identify trends and opportunities for business model adjustments towards sustainability.
Project actions
- 01Consider how data can inform your design choices.
- 02Explore tools or methods for analyzing user or market data to identify innovation opportunities.
Method & Evidence
Variables
Strengths & Limitations
Strengths
- +Addresses a critical gap in operationalizing business model innovation for sustainability.
- +Provides a practical artifact and demonstrates its utility through a case study.
Limitations
The complexity of big data tools might be a barrier for some projects; focus on accessible analytical methods.
Reliability & validity
The use of a critical case study provides depth but limits generalizability. Future research could employ multiple case studies or quantitative methods to enhance reliability and validity.
Think critically
To what extent can the 'democratization' of big data analytics truly empower small businesses, or are there inherent resource and expertise barriers that remain significant?
Design Principles
"Data-informed agility in business model design fosters continuous improvement and sustainability."
This approach provides a practical framework for organizations, especially smaller ones, to integrate data-driven insights into their strategic planning for sustainable business models. It democratizes access to advanced analytical tools, enabling more agile and responsive innovation.
What This Means for Your Design
Using big data to track how well a business is doing can help it come up with new ideas to be more sustainable and keep improving.
How to use in your project
- 1.Reference this study when discussing how data analytics can drive innovation in your design project, especially if sustainability is a goal.
Add to My Project
Quick Cite
Paragraph starter
The operationalization of business model innovation for sustainability can be significantly enhanced through the strategic application of big data analytics, as demonstrated by research showing that data-driven performance measurement fosters proactive and continuous innovation, even in smaller enterprises.
Source
Sustainability
Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations
journal · 2019
View sourceQuestions About This Research
- What does the research say about big data analytics accelerates sustainable business model innovation?
- Integrate big data analytics into performance measurement systems to create a dynamic feedback loop that informs and drives ongoing business model innovation for sustainability. Evidence: Sustainability (2019).
- Why does "Big Data Analytics Accelerates Sustainable Business Model Innovation" matter for design?
- This approach provides a practical framework for organizations, especially smaller ones, to integrate data-driven insights into their strategic planning for sustainable business models. It democratizes access to advanced analytical tools, enabling more agile and responsive innovation.
- How can designers apply this research?
- Integrate big data analytics into performance measurement systems to create a dynamic feedback loop that informs and drives ongoing business model innovation for sustainability.
- What were the main findings?
- The developed artifact effectively supports proactive and continuous business model innovation efforts.. The artifact's reliance on big data analytics is accessible and beneficial for small businesses, democratizing innovation practices.. Connecting business model innovation with performance management and big data analytics provides actionable paths for operationalization.
- What research method was used?
- Design Science Research Methodology.
- How strong is the evidence?
- Evidence strength is rated Strong effect, based on a 2019 journal from Sustainability.
- What should I do differently in my next project?
- Implement a performance dashboard that pulls data from various sources (sales, customer feedback, supply chain) and uses analytical tools to identify trends and opportunities for business model adjustments towards sustainability.
- What are the limitations?
- The study was based on a critical case study, and the artifact's generalizability to all types of businesses and industries may require further validation.