Research Paper Management approaches in the field of smart

Designing a Public Policy Dashboard for smart Governance: A Systematic Review

Fatemeh Ebrahimgol; Aliasghar Pourezzat; Mohammadreza Esmaeili Givi; sahar babaei

Volume 15, Issue 56 , June 2026, Pages 1-28

https://doi.org/10.22054/ims.2025.88230.2673

Abstract

A dashboard, as a visual display, plays an effective role in enhancing knowledge and information sharing across various domains. The use of dashboards in the field of policy, equipped with modern data-driven technologies and aligned with the expansion of e-government, creates a platform for achieving ... Read More

Research Paper Management approaches in the field of smart

Analyzing e-Participation Models Based on Giddens’ Structuration Theory

Mohammad Taghi Taghavifard; Molood Mehrabian; Payam Hanafizadeh; Jahanyar Bamdadsoofi

Volume 15, Issue 56 , June 2026, Pages 29-55

https://doi.org/10.22054/ims.2026.89069.2704

Abstract

and enhancing digital governance. However, existing models have paid limited attention to the dual interaction between structure and agency. This study aims to analyze the role of Giddens’ Structuration Theory in the development of e-participation models by integrating insights from high-quality ... Read More

Research Paper Data, information and knowledge management in the field of smart business

A Multilingual BERT Framework for Intelligent Screenplay Analysis: Emotion Recognition through Character Behavioral Patterns

Zohreh Jafarbeglou; MohammadAli AfsharKazemi; soheila jokar

Volume 15, Issue 56 , June 2026, Pages 55-84

https://doi.org/10.22054/ims.2026.87943.2666

Abstract

This study introduces EmoBERTScr, an intelligent Multilingual BERT-based model for detecting and analyzing emotional and behavioral patterns in Persian and multilingual screenplays. A dataset of 35,800 dialogue samples from 1,700 Persian and English screenplays was manually collected and annotated by ... Read More

Research Paper Management approaches in the field of smart

Multi-Level Governance of Power in Digital Platforms: A Systematic Meta-Synthesis Study

Atoosa Ebrahimi ShahAbadi; Ali Mobini Dehkordi

Volume 15, Issue 56 , June 2026, Pages 85-112

https://doi.org/10.22054/ims.2026.90886.2752

Abstract

Digital platforms have become complex structures in which technological architectures, network effects, and multi-sided interactions have created new forms of power at the macro, meso, and micro levels. This study aims to reorganize the scattered dimensions of platform-power governance into a coherent ... Read More

Research Paper Management approaches in the field of smart

The Effect of Digital Transformation on Startups’ Innovation Performance

Fatemeh Saghafi; foad faizy bagejan

Volume 15, Issue 56 , June 2026, Pages 113-134

https://doi.org/10.22054/ims.2026.88173.2674

Abstract

Digital transformation has been widely recognized as a critical driver of high-quality development in firms and a fundamental enabler of innovation-oriented strategies. Despite its growing significance, prior research has not sufficiently clarified the mechanisms through which digital transformation ... Read More

Research Paper Data, information and knowledge management in the field of smart business

The Role of Data, Information, and Knowledge Management in the Development of Intelligent Businesses: Evidence from 15 Developing Countries

Pariya Alihosseini; Mohammad Baradaran

Volume 15, Issue 56 , June 2026, Pages 136-144

https://doi.org/10.22054/ims.2026.88647.2684

Abstract

In recent years, the rapid growth of information and communication technologies and the increasing volume of data have turned data, information, and knowledge management into a core driver of intelligent business development. This study examines the role of data, information, and knowledge management ... Read More

Research Paper Data science, intelligence and future analysis

From Determinism to Technological Impetus: A Strategic Roadmap for Reconfiguring University Culture in the Age of Artificial Intelligence

Mohammad Hoseini Moghadam

Volume 15, Issue 56 , June 2026, Pages 145-165

https://doi.org/10.22054/ims.2026.89583.2712

Abstract

Artificial intelligence, as a principal driver of transformation in recent years and those to come, has redefined the role of universities in global competition and in the provision of scientific and educational services. Aiming to design a strategic roadmap for reconfiguring university culture in this ... Read More Artificial intelligence, as a principal driver of transformation in recent years and those to come, has redefined the role of universities in global competition and in the provision of scientific and educational services. Aiming to design a strategic roadmap for reconfiguring university culture in this context, this article examines changes in norms, roles, and customary practices in higher education. This transformation rests on three pillars—transformed leadership, faculty readiness, and access to advanced infrastructures—and has been reinforced by the post COVID acceleration of digital transformation in higher education. Methodologically, the study adopts a conceptual synthesis approach and proposes a staged model to explain cultural transformation. The model comprises three successive stages: first, an organizational flexibility stage that emphasizes local meanings and actor networks; second, an institutional consolidation stage in which rules and procedures are stabilized; and third, a technological impetus stage in which data driven infrastructures and emerging information architectures play a decisive role in shaping organizational culture. Across all stages, mediating and moderating mechanisms—including transparent data governance, algorithmic accountability, enhanced digital competence and literacy among faculty members, and equitable access—play a pivotal role. Building on this model, the article proposes an operational, stage based roadmap for policymaking, implementation, and evaluation, organized around three axes: strengthening transparent and effective data governance, enhancing the digital competence of all university actors, and reducing inequalities in access to infrastructures and opportunities in Iranian universities. The roadmap is designed to support the responsible adoption of AI while preventing the reproduction of existing educational inequalities and fostering a culture of accountability, equity, and lifelong learning.

Research Paper Management approaches in the field of smart

Intergenerational innovation in family firms: A conceptual framework for two-generational innovation and overcoming transition barriers

Maryam Vajdi Vahid

Volume 15, Issue 56 , June 2026, Pages 167-196

https://doi.org/10.22054/ims.2026.86948.2644

Abstract

Family firms face a fundamental challenge at the point of generational transition: How can they facilitate the necessary innovations for the survival and growth of the second generation (G2) simultaneously preserving the legacy and knowledge of the first generation (G1)? This research examines the mechanisms ... Read More

Research Paper Management approaches in the field of smart

Competing on the Edge of Technology: Creating Competitive Advantage for Knowledge-Based Firms in the Market

Fatemeh Jafari Bazyar; Abolhassan Hosseini; Mohammadreza Jalilvand

Volume 15, Issue 56 , June 2026, Pages 197-228

https://doi.org/10.22054/ims.2026.89029.2701

Abstract

Given the growing role of knowledge-based firms in economic growth, job creation, and the development of new technologies, examining the factors influencing their competitiveness particularly in the field of information technology is of great significance in today’s Iranian economic landscape. ... Read More

An Analysis of Political Coherence Cycle in Information and Communication Technology Governance System in Iran

Volume 4, Issue 16 , April 2016, , Pages 1-33

https://doi.org/10.22054/ims.2016.5147

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 ... Read More

The Role of Social Media on Entrepreneurship Intention

Volume 8, Issue 29 , November 2019, , Pages 35-60

https://doi.org/10.22054/ims.2019.10375

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, ... Read More

Political Foundation of Decision-Making in Formation of Market-Oriented Strategic Collaboration between Start-ups

Volume 6, Issue 22 , May 2018, , Pages 23-47

https://doi.org/10.22054/ims.2018.8519

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 ... Read More

Artificial Intelligence and The Future of Scientific Progress: From Normal Science to Post Normal Science

Volume 12, Issue 45 , September 2023, , Pages 71-116

https://doi.org/10.22054/ims.2023.74099.2341

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, ... Read More 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.IntroductionThroughout 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.MethodologyA 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.FindingsAI 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.ConclusionAI 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.RecommendationsTo 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.

The Presentation of a Pattern for Service Design and Development Process with a Competitiveness Approach in Iran Banking Industry

Volume 10, Issue 36 , August 2021, , Pages 71-113

https://doi.org/10.22054/ims.2021.55911.1832

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 ... Read More

Research Paper

Developing a Smart Policy-Making Framework to Enhance Organizational Agility in Innovative Companies: A Qualitative Data-Based Approach

Raheleh Jalalniya; Mohsen Akbari

Articles in Press, Accepted Manuscript, Available Online from 25 April 2026

https://doi.org/10.22054/ims.2026.87107.2646

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 ... Read More

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Research Paper

Identifying Inhibitors of Smart Tourism Development in Iran (Fuzzy Delphi Technique)

Fatemeh Karaji; Mohammad Javad Jamshidi; Mahdi Hosseinpour; Feyzallah Monavvarifard

Articles in Press, Accepted Manuscript, Available Online from 19 May 2026

https://doi.org/10.22054/ims.2026.86049.2620

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 ... Read More

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Research Paper

Attention Economy in Digital Age: Brands' Battle for Customer Attraction in Smart Businesses

Abdullah Saedi; Fatemeh Hadadi; Sara Miir

Articles in Press, Accepted Manuscript, Available Online from 03 June 2026

https://doi.org/10.22054/ims.2026.90917.2754

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 ... Read More

Research Paper

Explaining the Integrated Business Management Framework: Combining Osterwalder's Business Model Canvas and Bernard Marr's SMART Model

SHahab Razban; sahar Kousari; Mohammad Reza Sanaei

Articles in Press, Accepted Manuscript, Available Online from 07 June 2026

https://doi.org/10.22054/ims.2026.89676.2716

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 ... Read More 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.

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