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  
 
In all countries, entity or entities are responsible for the governance in the field of Information and Communication Technologies (ICT). In Iran, the governance of this area is the responsibility of several institutions which they must consider a set of elements and their relationships at the national level. The main objective of this paper is to institutionally analyze Iran's ICT governance system based on the cycle of cohesion policy and reviewing the existing challenges. With the review of the literature, a model for cohesion policy has been extracted. Based on this model and extensive research in the literature and interviews with experts in this field and by identifying responsible institutions in the governance of ICT and explaining their key roles in making policies the current situation and institutional challenges in each phase of the model is analyzed. Finally, to overcome the challenges ahead, some suggestions are presented.
 
 
 



 


 


 
 
 


 

Abstract  
The present study seeks to evaluate the effect of social media on people's entrepreneurial intention. In this study, the components of entrepreneurial intention are derived from the Linan’s Entrepreneurial Intention Model. These components include attitudes towards entrepreneurial behaviors, social norms, and self-belief. The present study is quantitative and the research method is descriptive. In this research, questionnaire was used for data collection.  Students of Allameh Tabatabaei University in Tehran are samples of the study. Gathered data was analyzed by using t-test, factor analysis, and structural equation model with SPSS and LISREL software. Results showed that there was a significant relationship between social media and entrepreneurial intention and its components. Furthermore, social media strongly influences entrepreneurial intention and its components. Moreover, the most significant impacts were related to entrepreneurial self-belief, social norms, and attitudes towards entrepreneurial behavior, respectively. Finally, all hypotheses were confirmed by structural analysis and structural equation modeling.
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Abstract  
Formation of strategic collaboration is process influenced by the political behavior because of the multiplicity of stakeholders and political action among them. This study aims to investigate the political foundation of decision making by focusing on the role of political skill and political environment on political behavior. To achieve this goal, the data is gathered based on random sampling of 68 market-based strategic collaborations between start-ups across the country. The impact of political skill and political environment is analyzed in political behavior related to decision-making with intermediate role of social capital and moderating role of political will. The findings of research indicates how political elements interact to each other in order to influence in social decision-making which is rooted in the origins of decision theory and the social influence theory. Political skills leads to positive political behavior in decision-making associated with the formation of strategic collaboration directly and through the creation of social capital while the political environment provides political bed-destructive behavior. In addition, actual results confirm moderating role of political will on the political skills in direct contact with the political behavior.
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Abstract  
The touch of the product plays an important role in the final decision of the customer when purchasing from physical and online retail, and the sensations that come to be enjoyed through touch enable them to experience the product from all angles. Therefore, considering the importance of touch, this research has investigated the lived experience of touching the product from the point of view of customers of physical and online stores. The following article is done with qualitative method and phenomenological paradigm. The research community is made up of electronic and clothing buyers from online and physical stores: Technolife, Adak, Havadar and Happyland in Tehran, and through semi-structured interviews, evidence was collected based on the purposeful sampling method. The interviews continued until reaching the theoretical saturation, and in this research, the interviews reached saturation with 15 people. Based on the extracted results, the main themes include; Product perception is physical touch, virtual touch, touch experiences, need for touch and touch perceptions. According to the results, managers of physical and online stores should provide conditions (such as the use of modern technologies) that touch and contact with the product happen to both groups of online and physical buyers so that they can buy products based on their needs and wants, and also this research can pave the way for the development of touch literature for researchers.

Introduction

Throughout human history, the idea of progress has been a central concern for thinkers and intellectuals, with technological advancements playing a pivotal role in shaping the development of societies (Du Pisani, 2006; Rivers, 2002). Artificial intelligence (AI), as a driving force behind the fourth industrial revolution, has had a profound impact on numerous fields, including scientific research and discovery (Velarde, 2020). AI has revolutionized scientific knowledge to such an extent that distinguishing between the discoveries made by intelligent machines and human experts has become increasingly difficult (Krenn et al, 2022). This article explores the implications of AI for the future of scientific progress and its potential to give rise to post-normal science.
Here is my attempt at rewriting the text as a senior researcher:
The central question examined in this article is: what role does AI play in shaping the future of scientific developments? In exploring this overarching question, several related questions are also considered: How can AI be leveraged to uncover and obtain new scientific knowledge? Can novel computing techniques based on AI not only detect unusual patterns and events in data, but also lay the groundwork for new scientific advances? Might AI furnish new theories and transform our comprehension of science? Can AI-based scientific systems determine which scientific questions are worthwhile, and for whom are they valuable? Looking ahead, what assurances will scientists have about the validity of AI-based analyses in science?
In response to these pressing questions, the core hypothesis presented is that AI has become the foundation for the emergence of a new breed and style of scientific discovery, which can be characterized as post-normal science. To evaluate this hypothesis, the historical background of relevant research is reviewed. AI represents a seismic shift in the practice of science, enabling analyses and discoveries that would be impossible for humans alone. While promising, it also poses troubling philosophical questions about the nature of truth and scientific understanding.

Methodology

A variety of research methods were employed to address the questions raised in this study, including a systematic review of relevant literature to identify the transition from normal to post-normal science, trend analysis to examine the influence and expansion of AI in scientific discoveries, documentary studies to obtain theoretical and conceptual foundations, and modeling to understand and describe the progress of post-normal science under the influence of AI.

Findings

AI has facilitated a new model of scientific discovery, known as data-driven scientific discovery, which derives hypotheses from data rather than relying on preconceived assumptions (Wheeler, 2004). This approach has transformed traditional sciences into data sciences, with scientific patterns extracted from data and an increasing focus on intelligent automation in scientific progress (King & Roberts, 2018). As a result, a new type of epistemology has emerged, characterized by the involvement of machines in scientific discovery and the advancement of the science cycle. This development, referred to as "Science 0.4" or the fourth type of science, has integrated science into society, enabling every citizen to participate as a scientist and fostering a shift towards "open science" (Odman & Govender, 2021).
AI's impact on scientific research has been guided by several key principles, including sustainability, different forms of knowledge, accountability and responsibility, values and interests, collective wisdom and rationality, and non-determinism and non-linearity in the process of scientific discovery. AI has contributed to the realization of post-normal science by facilitating simulation and modeling, improving decision-making, promoting ethics, embracing diversity, fostering interdisciplinary collaboration, expanding stakeholder engagement, and enabling big data analysis.

Conclusion

AI systems have fostered interdisciplinary collaborations and facilitated the integration of knowledge and expertise across various fields, allowing for the identification and resolution of complex, interdisciplinary scientific issues. This collaboration disrupts the linear progression of normal science, promoting a more integrated and cooperative approach to problem-solving. Furthermore, AI has introduced new ethical and social considerations in scientific research, necessitating a departure from conventional forms of normal science. Although it remains uncertain whether AI will replace the human role in scientific discovery, it is clear that scientists and institutions that embrace AI technology will surpass those that do not.

Recommendations

To achieve excellence in the field of AI within scientific institutions, it is crucial to understand the "state of maturity in AI" and to establish a starting point for the governance system of science and its actors. In this process, scientific institutions can be categorized along a spectrum, ranging from those seeking to familiarize themselves with AI-driven changes in scientific discovery to those actively leveraging AI technology to advance scientific knowledge.
Keywords: Artificial Intelligence, Normal Science, Post Normal Science, Science Progress, Scientific Discoveries.
 
 
 

Abstract The main purpose of the study is to explore the important issues and their relations to present a pattern for design and development process of financial services with a competitiveness approach in Iran banking industry. The study applies mixed method as its methodology. The qualitative section approach is based on Grounded Theory strategy. The data-collection instrument consists of semi-structured interview with twelve research and innovation experts and managers in banking industry selected through purposeful as well as snowball sampling. Dimensions and components of design and development process of financial services were extracted and compiled in a Grounded Theory conceptual model. The main phenomenon, process design and development services in the field of banking. Lastly, the final model were formulated and presented with regard to casual, intervening and contextual conditions as well as strategies and results.
In quantitative section, the data was collected by a researcher-made questionnaire consisting of 236 items and the descriptive-survey method was used for analysis and explanation of proposed pattern. Finally, the findings of the study shows that the managers and policy-makers of banking industry should consider all casual, intervening and contextual conditions as well as strategies and outcomes for successful development of banking modern products and services; They also should step into the identified phases of modern service development with a competitiveness and market-based approach.

Abstract This research has been conducted with the purpose of employing information technology (IT) in organizations. To this end, the present research, titled “The Impact of Characteristics of Employing IT on the Organizational Performance and the Competitive Advantage in Organizations”, was done, thereby resulting in achievement of the conceptual model of research. This model entails three factors or hidden variables, namely, IT practicing, organizational performance, and competitive advantage, each of which has some indicators. IT has four indicators: economic, security, Correctness & authenticity of information, and speed of information and communications. Organizational performance has also four indicators, i.e. customer, finance, human resources, and organizational efficacy. Competitive advantage also consist of five indicators, that is, price/cost, quality, timely delivery of product or service, and innovation & time of delivery to the market.
     Based on this model, the Questionnaire for measuring the indices was designed and distributed among 85 managers of IT and information systems nationwide and collected after completion. Data obtained from this Questionnaire regarding research hypotheses in the form of a single model suggest that IT factors affect organizational performance and competitive advantage in addition to security.
 

Abstract This article aims to delineate health management system functions and also introduce current Iran's health system. Three basic applications of information and communications technology applications in the medical field-e health, telemedicine and mobile healthcare going to be explained by reviewing previous studies in regard with contemporary conditions in Iran. Considering this fact that growing telecommunications & informatics industry have brought a revolution in the development of all industries, leads us to this conclusion that the development of these technologies in medical sectors can bring a huge up heavily in health management systems in the near future Storage systems for patient information, medical information systems, medical& surgical procedures, tracking systems management, remote management systems, guiding nurses systems, surge on robots, patient reservation, and many others systems have a common goal which facilitates health management by information technology. Universal access to medical information in the context of electronic networks is not anymore a far dream.