Abstract The aim of this research is to design and validate a data-driven decision-making framework for integrated business model management by aligning Osterwalder's Business Model Canvas with Bernard Marr's SMART framework. The research gap in the literature shows that although the Business Model Canvas is a powerful tool for designing value creation logic and the SMART model provides a practical framework for data analysis and performance improvement, a systematic mechanism for structurally connecting the two in the form of a continuous learning cycle has not been presented so far.
This research is of a developmental-applied type and based on conceptual modeling. Data were collected through qualitative content analysis of 86 scientific sources and 18 semi-structured interviews with industrial and academic experts. Coding was done at three levels: open, axial, and selective. The proposed framework was developed in the form of a three-layer architecture including a design layer (BMC), a measurement and analysis layer (SMART), and a data governance and continuous improvement layer. Model validation was performed using a three-stage Delphi technique. Kendall's coordination coefficient (W=0.71) and CVR higher than 0.62 for all components indicate acceptable expert consensus.
The proposed model creates an organizational learning cycle of “design → measurement → analysis → decision → redesign” that bridges the gap between strategic design and data-driven execution.
1.Introduction
The introduction presents a research problem that focuses on the lack of a systematic framework that connects Osterwalder’s Business Model Canvas (BMC) with Bernard Marr’s SMART framework. Although BMC is widely used for designing business models and value creation logic, and SMART is effective for measuring, analyzing data, and improving performance, the two are separate in theory and practice. This lack of integration limits organizations’ ability to transform strategic design into continuous, data-driven improvement.
This study argues that existing approaches do not provide a clear mechanism for linking business model design with organizational learning and performance feedback. To address this gap, this research aims to design and validate a data-driven decision-making framework for integrated business model management. The proposed model is built as a three-layer architecture that includes a BMC-based design layer, a SMART-based measurement and analysis layer, and a data governance and continuous improvement layer.
Overall, the introduction emphasizes the need for a framework that links strategic planning and empirical decision-making, enabling organizations to follow a continuous cycle of design → measurement → analysis → decision-making → redesign.
Research Questions:
How can Osterwalder’s Business Model Canvas (BMC) and Bernard Marr’s SMART framework be systematically integrated into a unified model for business management?
What structural layers and relationships are required to connect business model design (BMC) with data-based measurement and performance improvement (SMART)?
How can a data-driven decision-making framework enable continuous organizational learning and improvement through a cycle of Design → Measure → Analyze → Decide → Redesign?
To what extent is the developed framework valid and reliable according to expert consensus evaluated through Delphi analysis (using Kendall’s W and CVR indicators)?
To what extent is the developed framework valid and reliable according to expert consensus evaluated through Delphi analysis (using Kendall’s W and CVR indicators)?
Literature Review
Literature highlights two distinct, highly influential streams in business management: the Business Model Canvas (BMC) and the SMART framework. The BMC, primarily associated with Osterwalder, is extensively documented as a robust tool for visualizing, analyzing, and designing the logic of value creation, delivery, and capture. Parallel to this, the literature surrounding Bernard Marr’s SMART model offers a comprehensive paradigm for structured data management, performance metrics, and data-driven decision-making. However, a critical synthesis of these bodies of work reveals a significant research gap: while both frameworks are theoretically mature, they currently operate in silos. Existing literature lacks a systematic, architectural integration that bridges the gap between static strategic design and dynamic, evidence-based performance measurement. Consequently, there is no established organizational learning mechanism that effectively embeds data governance and continuous feedback loops into the heart of the business model. This study addresses this academic and practical void by synthesizing these two frameworks into a unified, three-layer conceptual model aimed at fostering continuous organizational improvement.
Methodology
This study adopts a developmental–applied research design supported by a conceptual modeling approach to construct and validate an integrated framework linking the Business Model Canvas (BMC) with the SMART model. The methodology consisted of two primary phases. First, a qualitative content analysis of 86 academic and professional sources was conducted to extract relevant constructs, processes, and theoretical relationships. Second, 18 semi-structured interviews with academic and industry experts were carried out to capture practical insights and ensure the framework’s applicability. The collected data were analyzed through open, axial, and selective coding, leading to the development of a three-layer architecture consisting of a design layer, a measurement and analysis layer, and a data governance and continuous improvement layer. To assess the validity of the proposed model, a three-round Delphi study was performed, yielding strong expert consensus, with Kendall’s W = 0.71 and CVR values above 0.62, confirming the reliability and relevance of the framework.
If you’d like, I can also format it in a more formal academic tone or expand it into a full methodology section with subsections (e.g., research design, data collection, analysis).
Results
The analysis resulted in the development of an integrated framework for business model management that systematically links Osterwalder’s Business Model Canvas (BMC) with Bernard Marr’s SMART model. The findings demonstrate that an effective connection between strategic business model design and data-driven performance management can be achieved through a structured, multi-layer architecture. The proposed framework consists of three interrelated layers: (1) a design layer, grounded in the BMC, which captures the organization’s value creation logic and key business model components; (2) a measurement and analysis layer, derived from the SMART model, which translates strategic assumptions and value propositions into measurable indicators and analytical processes; and (3) a data governance and continuous improvement layer, which ensures the integrity, accessibility, and traceability of organizational data while facilitating feedback mechanisms between the design and analytical components.
The qualitative analysis process generated 124 initial codes, which were subsequently organized into 16 axial categories and ultimately synthesized into five overarching themes. These themes provided the conceptual foundation for constructing the proposed framework and for establishing the block–indicator matrix that operationalizes the relationship between business model elements and measurable performance indicators. Within this structure, the BMC functions as a mechanism for formulating value-creation hypotheses, while the SMART framework enables the identification of relevant metrics, the collection and analysis of organizational data, and the interpretation of performance outcomes.
The results further indicate that the integration of these components enables organizations to implement a systematic learning and improvement cycle. In this cycle, business models are initially designed using the BMC, subsequently evaluated through data-driven measurement and analysis using the SMART approach, and then refined based on analytical insights and performance feedback. This iterative process establishes a continuous organizational learning loop—Design → Measure → Analyze → Decide → Redesign—which strengthens the alignment between strategic intent and operational performance. Overall, the findings highlight the capacity of the proposed framework to bridge the traditional gap between business model design and evidence-based managerial decision-making.
Discussion and Conclusion
The findings confirm that integrating Osterwalder’s Business Model Canvas (BMC) with Bernard Marr’s SMART framework provides a coherent structure for linking strategic design with data-driven decision-making. The proposed three-layer model—design (BMC), measurement and analysis (SMART), and data governance—bridges the gap between conceptual business model development and empirical performance evaluation. The study demonstrates that translating value-creation hypotheses from the BMC into quantifiable indicators through the SMART model enables organizations to monitor, assess, and refine their business models based on reliable evidence. This integration establishes a continuous learning cycle of Design → Measure → Analyze → Decide → Redesign, fostering dynamic adaptation rather than static strategy execution. The Delphi validation confirms the model’s conceptual robustness and practical feasibility (Kendall’s W = 0.71; CVR > 0.62). The framework thus contributes to both theory and practice by providing a structured mechanism for continuous improvement and strategic alignment. It supports organizations in transforming business model management from an intuitive, one-time activity into an iterative, data-driven process. Overall, the research offers a validated foundation for bridging strategic design and analytical performance management, while future studies may refine or extend the model through quantitative testing and sector-specific applications.
