A Systems Thinking based Sustainable Business Model Framework –An Appropriate Approach for the Design of Sustainable Business Models in Start-Up Consulting
Bibliographic record
Abstract
The Business Model Canvas (BMC), originally developed by Osterwalder and Pigneur, has become a well-established and widely utilised tool for the development, modification, and visualisation of business models. While the Business Model Canvas provides an effective framework for designing business models, there remains a need to enhance the understanding of cause-and-effect relationships within the system, as well as to establish a holistic perspective on its impacts. This is particularly relevant for entrepreneurs and start-up advisors, as start-ups typically have fewer financial resources to mitigate or adapt to unsuitable decisions taken than established companies. Furthermore, many of today’s start-ups operate in highly complex and dynamic sectors, such as the digital economy, and often strive for a holistic view of sustainability, balancing economic, environmental, and social impacts. This paper reviews the traditional Business Model Canvas and explores sustainable business models grounded in the Triple Bottom Line approach. It then introduces a Systems Thinking based Sustainable Business Model framework (STSBM) that offers a robust methodology for designing sustainable dynamic modern business models. Specifically, impact networks are proposed to align mental models, while scenario simulation and evaluation are presented as tools for managing complexity. The paper outlines the process of model development, details the structural elements of the proposed framework, and suggests its practical application. By integrating these principles, the proposed framework aims to support the creation of sustainable business models that effectively address the intricate challenges faced by start-ups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".