Business Intelligence Management Studies

Abstract This study aims to develop a smart policy-making framework to enhance organizational agility in innovative companies, using a qualitative data-based approach. The research is applied-developmental in nature and employs a descriptive, non-experimental approach to data collection. The study participants included 17 experts: theoretical experts (professors in IT management and entrepreneurship) and practical experts (managers of innovative companies) who had knowledge of smart policy implementation. Qualitative data were gathered through semi-structured interviews based on six main questions, with the possibility of follow-up questions. Data were analyzed using the grounded theory method with the assistance of MAXQDA software. Subsequently, the fuzzy Delphi method was employed in MATLAB to screen the research indicators. Based on the proposed model, causal conditions—including environmental dynamism and transformative pressures, customers’ innovative demands, the advancement of smart technologies, and the need for data-driven governance—affect the core phenomenon of smart policy-making. This core phenomenon, along with contextual conditions (such as the maturity of internal digital infrastructure and an open, learning-oriented organizational culture) and intervening conditions (such as institutional barriers and organizational inertia to change), influence strategies and actions (such as developing a smart policy architecture aligned with digital transformation). These strategies and actions ultimately lead to outcomes such as enhanced organizational agility, strengthened systemic and collaborative innovation, and increased social capital and institutional trust.

Abstract Given the increasing role of new technologies in the transformation of the tourism industry and its importance in sustainable economic and social development, utilizing smart tourism capacities in countries has become an undeniable necessity. The aim of this research is to systematically identify and analyze the obstacles to the development of smart tourism in Iran in order to provide effective solutions to overcome them. This research is of an applied type and was conducted using the triangular fuzzy Delphi method in three rounds. The statistical population consisted of 28 experts who were selected using a purposive sampling method of chain referral. The data collection tool was a researcher-made questionnaire whose items were extracted from the content analysis of interviews and 21 scientific articles and validated based on the PRISMA flowchart. The validity and reliability of the research tool were confirmed by conducting three rounds of triangular fuzzy Delphi and calculating the agreement coefficient. The research findings indicate that the obstacles to the development of smart tourism in Iran are categorized into eight main dimensions: technical and infrastructural, institutional-legal, human resources, economic, international, educational, cultural-social, and security. Accordingly, solutions have been proposed to remove these obstacles and accelerate the process of making tourism smart in Iran. This research, for the first time, uses the triangular fuzzy Delphi technique to comprehensively identify and prioritize the obstacles to the development of smart tourism in Iran and provides a model for policymaking in order to promote smart tourism and achieve sustainable development.

Abstract The attention economy is an analytical framework that considers human attention as a scarce and limited resource and examines how businesses compete to attract, allocate, and exploit this resource in the age of technology. The present study was conducted with the aim of Attention Economy in the Digital Age: Brands' Battle for Customer Acquisition in Smart Businesses. The statistical population of the study is professors and managers of smart businesses, who were selected using a purposive sampling method of 22 people. The data collection tool in the qualitative part was an interview, the validity and reliability of which were assessed using content validity and intra-coder and extra-coder reliability methods. In the quantitative part, the data collection tool was a questionnaire, the validity and reliability of which were confirmed by content validity and test-retest reliability. It should be noted that the qualitative data were examined using MaxQD software and the content analysis method, and the quantitative data were analyzed using the fuzzy cognitive map method. The findings indicate that search engine optimization, brand storytelling, brand authenticity, collaboration with influencers, advertising targeting algorithms, and gamification are, respectively, the most important factors influencing the attention economy in smart businesses

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.

Abstract Objective: Digital transformation in higher education necessitates empirical evidence for managerial decision-making. This study designed and validated a comprehensive Process Mining (PM) framework for the University of Tehran to bridge the gap between technical analytics and institutional governance.
Methodology: A multi-method approach was employed, including a systematic review, meta-ethnography of eight reference models (e.g., CRISP-PM, L* Lifecycle), and a qualitative case study. A synthesized 6-phase conceptual model was refined into a 10-phase operational framework through semi-structured interviews with 18 academic experts.
Findings: Effective PM implementation requires alignment across technical, managerial, and learning layers. The framework was applied to the "PhD Proposal Approval" process using the Heuristic Miner algorithm. Analysis revealed a 325-day average cycle time caused by structural bottlenecks and manual plagiarism checks. Monte Carlo simulations demonstrated that process optimization and parallelization could yield a 43% efficiency gain, reducing the duration to under 200 days.
Conclusion: The proposed 10-phase framework elevates PM from a diagnostic tool to a robust governance protocol. Institutionalizing this model via real-time monitoring dashboards facilitates data-driven governance and enhances the student academic experience at the University of Tehran.

Abstract With the increase in the use of smart phones and tablets, mobile banking has gradually become one of the attractive channels for banks to provide services to their customers. Banks expect users to fully utilize the services and potential of the bank's companion in order to achieve the expected benefits. An increase in the amount of use does not always mean an increase in profit and investment benefits. But it is the behaviors based on the infusion of information technology that require people to use IT in a deep and comprehensive way. Therefore, the purpose of this research is to design a mobile banking infusion model among banking service users. Therefore, the purpose of this study is to design a model of mobile banking penetration among banking service users. The research method is qualitative and based on a data-driven method. After interviewing 16 mobile banking users, theoretical saturation was achieved and using MAXQDA software and open, axial and selective coding, 638 concepts, 67 subcategories and 25 core categories were identified and a paradigmatic model was drawn and propositions were formulated. The results of the study showed that the dimensions of the central phenomenon of the study (Mobile Banking Penetration) include the widespread use of mobile banking features, coherence and integration with mobile banking, and the emerging use and innovation of mobile banking users.

Abstract This study aimed to develop a model for the adoption of artificial intelligence (AI) in financial risk management across the gold value chain. The study was designed using a constructivist grounded theory approach with an interpretivist orientation and was aligned with the “Research Onion” logic to harmonize philosophy, strategy, and method. Data were collected through in-depth semi-structured interviews with purposively and then theoretically sampled experts from different segments of the gold value chain; data collection and analysis proceeded concurrently. Analysis employed initial coding, focused coding, and theorizing, alongside constant comparison and analytic memoing. Trustworthiness and dependability were enhanced through member checking, peer debriefing, and inter-coder agreement. The findings revealed a core process of human–machine hybrid financial decision-making, shaped by the dynamic interplay of three dimensions: building trust and the gradual adoption of AI; data-driven governance and infrastructure; and financial risks and the strategies for managing them. In this configuration, AI functioned as a decision-support tool while humans remained the final arbiter; trust in models was contingent on data quality, explainability, and contextual fit, and implementation outcomes reflexively recalibrated trust levels and governance arrangements. The contribution lies in proposing a context-sensitive process model for the responsible integration of AI into financial risk management in the gold industry and in explicating the mechanisms of trust, governance, and human–machine integration, yielding practical implications for data governance design, workforce capability building, and the development of explainable models.