Management approaches in the field of smart
Samaneh Moradi; Mehrdad Hosseini Shakib; Ali Badizadeh
Abstract
In the era of the Fourth Industrial Revolution, digitalization and implementation of emerging technologies are considered as the main drivers of transformation in various industries. The railway transportation industry is no exception to this rule and requires precise criteria to assess its maturity ...
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In the era of the Fourth Industrial Revolution, digitalization and implementation of emerging technologies are considered as the main drivers of transformation in various industries. The railway transportation industry is no exception to this rule and requires precise criteria to assess its maturity level and readiness in adopting and implementing Industry 4.0 related technologies. The main objective of this research is to identify and present a conceptual model for railway transportation industry maturity assessment based on fourth-generation industrial technologies. This study was conducted using Sandelowski and Barroso's seven-stage meta-synthesis method and systematic review of 87 scientific articles published between 2016 and 2025. The article screening process was based on precise evaluation criteria including language, temporal scope, study conditions, research population, and article types. The research results led to the development of a conceptual model comprising 5 main dimensions, 21 indicators, and 84 operational codes, which include: Industry 4.0 technologies in railway transportation, digitalization challenges and barriers, cybersecurity and digital risks, practical applications and performance improvement, and sustainability and environment. The scientific validity of the results was confirmed with a Kappa coefficient of 0.89 and content validity of 0.83. This model provides a comprehensive framework for evaluating and measuring the maturity level of organizations active in railway transportation and can be used as an effective tool for identifying strengths and weaknesses, and developing improvement strategies in the digitalization path.
Management approaches in the field of smart
Aliasghar Pourezzat; Fatemeh Ebrahimgol; sahar babaei
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 ...
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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 smart governance, enabling progress toward transparency, accountability, forward-looking decision-making, and greater participation. In smart cities, participation often occurs through dedicated participation platforms, such as dashboards, through which citizens can vote, discuss, and brainstorm ideas. The aim of the present research is to provide a framework for designing a dashboard in public policy-making to realize smart governance. To this end, through a review conducted in the Scopus and Web of Science databases, 40 articles published between 2012 and 2025 focusing on dashboard design were selected and qualitatively analyzed. In this study, the design of managerial dashboards in various domains was observed, and subsequently, considering the multitude of features and approaches used, the design of dashboards in public policy-making was addressed from a broader perspective. The research findings, using a thematic analysis approach based on the Brown and Clarke (2006) framework, revealed that for designing smart public policy-making dashboards, 55 sub-themes could be categorized into 6 main themes. The main themes are: needs assessment, intelligent data collection and management, visual design, intelligent data analysis, technical capabilities, and intelligent evaluation and support.
Management approaches in the field of smart
Mahmoud Zahedian Nezhad; Mohammad Mehraeen; Rouhollah Bagheri; Seyyed Mohammad Tabatabaei
Abstract
Cardiovascular Diseases (CVDs) represent a primary cause of global mortality. The proliferation of complex data from diagnostic tools like ECG poses significant challenges for clinicians, affecting diagnostic accuracy and delaying treatment. While Ensemble Learning (EL) offers enhanced performance by ...
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Cardiovascular Diseases (CVDs) represent a primary cause of global mortality. The proliferation of complex data from diagnostic tools like ECG poses significant challenges for clinicians, affecting diagnostic accuracy and delaying treatment. While Ensemble Learning (EL) offers enhanced performance by integrating multiple models, a systematic comparison of its techniques within CVD management has been limited. This study utilizes a meta-synthesis to investigate the application of EL models, often combined with Machine Learning (ML) and Deep Learning (DL). The research aims to categorize EL models in CVD management, evaluate their performance, identify their advantages and limitations, and analyze the role of feature engineering. Our findings show that EL applications are classified into four domains: prediction, diagnosis, identification, and classification. The results confirm EL models are dominant across all categories, with their effectiveness heightened when integrated with ML and DL. Notably, Random Forest (RF) and gradient boosting models like XGBoost are the most frequently implemented and highest-performing techniques, consistently yielding superior results. This study offers valuable insights for researchers and clinicians, providing a framework for applying hybrid models to achieve more precise and effective management of cardiovascular diseases.
Management approaches in the field of smart
meysam davoodi; Seyed Ehsan Zahouri; bahram alishiri
Abstract
This study aimed to develop a comprehensive Artificial Intelligence (AI)-based policy implementation model for the Central Branch of the Social Security Organization (SSO) of Khuzestan Province. Given the extensive scope of the SSO’s services, leveraging AI is essential for enhancing transparency, ...
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This study aimed to develop a comprehensive Artificial Intelligence (AI)-based policy implementation model for the Central Branch of the Social Security Organization (SSO) of Khuzestan Province. Given the extensive scope of the SSO’s services, leveraging AI is essential for enhancing transparency, execution efficiency, and accountability in response to complex socio-economic challenges.Methodologically, this research is applied in objective and mixed-methods (qualitative-quantitative) in design. The qualitative phase utilized the Meta-Synthesis method to extract the model’s components. The sample comprised 30 scientific articles and documents (domestic and foreign), which, upon coding, led to the identification of 10 main components and 53 indicators.In the quantitative phase, the Fuzzy Delphi method was employed for validation, confirmation, and weighting of the components. The sample for this phase included 15 academic and executive experts in the fields of AI and Social Security.The Meta-Synthesis results indicated that the AI policy implementation model consists of 10 key components, the most significant of which include: “Analytical Capacity and Decision Support,” “Human Resources Requirements and Skills,” “Technology and Operations Integration,” and “Ethical, Legal, and Security Challenges.” Furthermore, the Fuzzy Delphi findings confirmed these components and prioritized them within the SSO context, establishing final implementation requirements. The resulting model provides a comprehensive 10-component framework that introduces AI tools for enhanced efficiency while emphasizing governance and ethics to facilitate responsible and accountable policy execution.Keywords: Meta-Synthesis, Fuzzy Delphi, Policymaking, Artificial Intelligence, Social Security Organization, Policy Implementation Model.
Management approaches in the field of smart
mohammad rabiei
Abstract
Semantic similarity is used in applications such as information retrieval, text summarization and sentiment analysis. In this article, a new method based on deep learning has been presented in order to check the matching percentage of the proposed name of the company registration applicants with the ...
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Semantic similarity is used in applications such as information retrieval, text summarization and sentiment analysis. In this article, a new method based on deep learning has been presented in order to check the matching percentage of the proposed name of the company registration applicants with the time of the company's activity. The key innovation lies in the use of a combined Aria BERT model for word embedding to convert registered company names into vectors. Additionally, the company's field of activity is converted into numerical vectors using the FastText model, which are then processed through deep learning algorithms, specifically bidirectional long short-term memory (Bi-LSTM) networks with an additional attention layer. The results were evaluated using cosine similarity and ROUGE criteria. Following the approval of the company name and activity field, the DBSCAN clustering method is employed to categorize the company names based on their activities. The results demonstrate that the ROUGE-1, ROUGE-2, and ROUGE-L scores for company activity vectorization are 0/7623, 0/7413, and 0/7982, respectively. The overall model accuracy and recall were 0/8512 and 0/8317, respectively. Moreover, the correlation coefficient between the cosine similarity of the proposed names and the company's activity time, as calculated by the model, was 93%, confirming the model's effectiveness.This method effectively preventing the registration of names that do not meaningfully relate to the company's operations. By clustering company names, the method facilitates the suggestion of related names based on the company's field of activity.
Management approaches in the field of smart
niloufar hadianfar; Ameneh Khadivar
Abstract
The emergence of artificial intelligence (AI) has sparked the development of a very important technological innovation in the rapidly developing world of conversational services, known as chatbots. In modern business, chatbots have become vital for interacting with customers and optimizing business processes. ...
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The emergence of artificial intelligence (AI) has sparked the development of a very important technological innovation in the rapidly developing world of conversational services, known as chatbots. In modern business, chatbots have become vital for interacting with customers and optimizing business processes. They are used in various fields, including customer service, marketing, sales, internal communications, and many others.Their importance lies in automating and facilitating interaction with users, which leads to improved service and efficiency of various business processes, opening up a wide range of opportunities. This study aims to systematically review research conducted in the field of chatbots and business marketing using a bibliometric approach. For this purpose, 1339 articles from the Scopus citation database were reviewed. The analysis of the articles was performed using VOS VIEWER software. Highly cited authors, institutions, and countries were identified, and 6 main clusters resulting from word co-occurrence analysis were identified: 1. Artificial intelligence and educational and linguistic applications 2. Human-machine interaction and socio-economic impacts 3. Digital transformation and applications of artificial intelligence in services and retail 4. Immersive technologies and their impact on customer experience and digital marketing 5. Attitude, behavioral intention and human experience in interaction with technology and 6. Artificial intelligence and data analysis in social media and crises. Finally, the clusters obtained were reviewed and suggestions for future research were presented.
Management approaches in the field of smart
Sakineh Ebrahimi; Bagher Asgarnezhad Nouri; Hooshmand Bagheri Garabollagh; Ramin Bashir Khodaparasti
Abstract
Encouraging online consumers to engage in voluntary extra-role behavior, referred to as "consumer citizenship behavior," is critical for organizational performance in modern society. With the increasing adoption of gamification in online environments, consumers are motivated to exhibit behaviors like ...
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Encouraging online consumers to engage in voluntary extra-role behavior, referred to as "consumer citizenship behavior," is critical for organizational performance in modern society. With the increasing adoption of gamification in online environments, consumers are motivated to exhibit behaviors like helping others and providing feedback. This study aims to explore the perceived affordances of gamification (autonomy, engagement, self-expression, and competition) on consumer citizenship behavior, with the mediating role of psychological ownership and the moderating role of consumer personality. The statistical population consisted of users of the gamified Snap platform in e-commerce. Since the exact population size was unknown, the sample size was determined as 384 individuals based on Cochran's formula at a 5% error level, selected through non-random convenience sampling. A standardized questionnaire measured variables. Its validity was assessed using construct, convergent, and discriminant validity, while reliability was evaluated via composite reliability and Cronbach's alpha. Data were analyzed using structural equation modeling with Partial Least Squares (PLS) software. Results indicated that perceived affordances of gamification (autonomy, engagement, self-expression, and competition) significantly and positively impact consumer citizenship behavior. Psychological ownership emerged as a crucial factor influencing this behavior in e-commerce. Furthermore, findings confirmed psychological ownership mediates the relationship between perceived affordances of gamification and consumer citizenship behavior. Lastly, the moderating effect of consumer personality on the relationship between psychological ownership and consumer citizenship behavior was validated.
Management approaches in the field of smart
Davood Feizِ; Azim Zarei; Mohsen Arman; Elham Sadat Kia
Abstract
This study examines the impact of artificial intelligence (AI) on social currency in digital marketing. By transforming traditional concepts such as identity, belonging, and loyalty through intelligent technologies, this research analyzes emerging dimensions of social currency and proposes an innovative ...
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This study examines the impact of artificial intelligence (AI) on social currency in digital marketing. By transforming traditional concepts such as identity, belonging, and loyalty through intelligent technologies, this research analyzes emerging dimensions of social currency and proposes an innovative framework for optimizing digital marketing strategies in the AI era. The primary focus is on AI's role in personalization, behavior prediction, and content generation, which has revolutionized the value derived from digital social interactions .Using a qualitative methodology and thematic analysis, data were collected through semi-structured interviews with 16 experts in marketing, management, and AI. The analysis process identified 140 initial codes, 20 organizing themes, and 5 overarching themes: transformation of social values in digital spaces, AI's role in developing social capital, AI's influence on consumer behavior, strengthening long-term customer relationships, and accelerating digital interactions. Findings indicate that AI enriches digital interactions by redefining social currency dimensions and providing tools for behavior prediction and loyalty enhancement. However, challenges such as privacy violations, algorithmic biases, and reduced human touch remain significant barriers. The study recommends that companies implement data analytics systems, conduct training programs, and establish ethical frameworks to manage these challenges and leverage AI's benefits.
Management approaches in the field of smart
Minasadat Mousavi; Abbas ali Rastgar; Mohsen Shafiei Nikabadi
Abstract
Purpos:This study aims to elucidate the mechanisms through which artificial intelligence (AI) impacts human resource management (HRM) from a positive psychology perspective and to propose a hierarchical framework for enhancing employee flourishing.Method:The applied-developmental research employed a ...
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Purpos:This study aims to elucidate the mechanisms through which artificial intelligence (AI) impacts human resource management (HRM) from a positive psychology perspective and to propose a hierarchical framework for enhancing employee flourishing.Method:The applied-developmental research employed a mixed-methods approach. The sample consisted of 15 experts in AI and HRM selected via purposive sampling. Data were collected through semi-structured interviews using an interview guide. Thematic analysis and Interpretive Structural Modeling (ISM) were applied for data analysis.Findings:A five-layer framework was developed encompassing strategic drivers, technological enablers, positive psychological constructs, learning processes, and organizational outcomes. The framework highlights that successful AI adoption requires integrating technological capabilities, algorithmic fairness, and psychological support for employees.Innovation:This research fills a gap in the smart HRM literature by offering a comprehensive, synergistic model that integrates strategic, technological, and psychological dimensions.Conclusion: AI implementation alone is insufficient; it must be accompanied by robust strategic infrastructure, data governance, and psychological empowerment to achieve simultaneous human flourishing and organizational productivity. The proposed framework serves as a practical guide for managers and a foundation for future quantitative studies.
Management approaches in the field of smart
seyed saba sinaei; Maghsoud Amiri; Laya Olfat; Amir Yousefli
Abstract
In recent years, digital transformations have significantly influenced supply chain processes, leading to an expansion of research in this field. Identifying the domains, emerging topics, and research trends within the digital supply chain can greatly assist scholars working in this area. This study ...
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In recent years, digital transformations have significantly influenced supply chain processes, leading to an expansion of research in this field. Identifying the domains, emerging topics, and research trends within the digital supply chain can greatly assist scholars working in this area. This study aims to clarify the conceptual structure of the digital supply chain and identify its research trends and key domains. To this end, a systematic literature review was conducted, collecting scientific articles indexed in reputable databases. After initial screening and applying specific criteria, a final set of selected articles was extracted. These articles were then analyzed using bibliometric analysis techniques, including co-occurrence and bibliographic coupling analyses. The results revealed a gradual growth in digital supply chain research, with increasing focus on technologies such as the Internet of Things, blockchain, and artificial intelligence, as well as themes related to sustainability, resilience, and digital collaboration. Eight conceptual clusters were identified, reflecting the technical, operational, organizational, and human dimensions of digital supply chains. The novelty of this study lies in combining bibliometric analysis with a systematic review of 425 articles, which has led to the identification of new conceptual clusters and the provision of a more comprehensive picture of the technological, organizational, and human dimensions of the digital supply chain. Overall, the findings of this study offer a comprehensive framework that enhances understanding of research trends and supports researchers, policymakers, and industry managers in aligning their future academic and practical directions more effectively.
Management approaches in the field of smart
Maryam Mollabagher; Alireza Hasanzadeh; Mohammad Mehdi sepehri; Abbas Habibelahi; Abolghasem Sarabadani
Abstract
The rapid advancement of digital technologies and the growing burden of neonatal diseases have underscored the need for intelligent solutions in modern healthcare systems. Retinopathy of Prematurity (ROP), a vision-threatening disorder in preterm infants, requires timely, accurate, and structured interventions; ...
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The rapid advancement of digital technologies and the growing burden of neonatal diseases have underscored the need for intelligent solutions in modern healthcare systems. Retinopathy of Prematurity (ROP), a vision-threatening disorder in preterm infants, requires timely, accurate, and structured interventions; yet existing care systems face persistent challenges in infrastructure, clinical processes, stakeholder coordination, and technology integration.This study proposes an organizational architecture framework for a smart ROP surveillance system, developed using the TOGAF ADM methodology. A mixed-methods design was employed: theoretical insights were obtained through a systematic literature review, analysis of policy documents, and examination of service identification cards, while semi-structured interviews with experts in healthcare, policy, and information technology helped to identify and refine key system components.Based on TOGAF 10, a multi-layered framework was constructed across four domains—business, application, data, and technology. The framework incorporates clinical datasets, decision-support tools, secure infrastructures, and governance mechanisms. Its validity was examined through the survey of experts and assessed using Moutinho’s evaluation criteria, ensuring conceptual rigor and practical relevance.The result of the research is a native and coherent framework that includes informational (clinical and diagnostic data), application (decision support systems), technological (communication and security infrastructure), and institutional (policy-making structures and key stakeholders) components, and provides the necessary platform for improving the quality of care, reducing human errors in diagnosis and therapeutic interventions, and advancing digital health strategies in the treatment of this disease. Finally, in addition to the research presentations, practical suggestions and recommendations for future research are provided.
Management approaches in the field of smart
Mohammad Faryabi; Samad Rahimiaghdam; Zahra Ranjbar Areshtanab; Zahra Ghorbanimoaddab
Abstract
In today's complex and competitive world, startups need strong and up-to-date knowledge management to identify opportunities and create innovation. In this regard, knowledge management can help startups create effective innovations by utilizing accurate information and using it in a timely manner and ...
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In today's complex and competitive world, startups need strong and up-to-date knowledge management to identify opportunities and create innovation. In this regard, knowledge management can help startups create effective innovations by utilizing accurate information and using it in a timely manner and maintain their position in the competitive market. Therefore, the purpose of this study is to examine the impact of knowledge management on the performance of Iranian startups, focusing on the mediating role of digital innovation and the moderating role of strategic flexibility, which has not been examined in previous studies. This study is quantitative and of the type of applied studies. The statistical population includes 1321 startups nationwide. Data was collected through a standard questionnaire and through the opinions of startup managers. Data analysis was evaluated using structural equation modeling and partial least squares. The findings reveal that knowledge management has a direct and positive effect on the performance of startups. Additionally, digital innovation strengthens the relationship between knowledge management and performance as a mediator, while strategic flexibility positively moderates this relationship. This research contributes by introducing a new model to improve startup performance, demonstrating that digital innovation and strategic flexibility are critical factors in enhancing the effect of knowledge management on startup performance.
Management approaches in the field of smart
Hadi Taghavi; Mohammad Mehraeen; Mehdi ShamiZanjani; Alireza Khorakian
Abstract
The digital transformation of the banking sector is essential for survival and competitiveness in the digital age. This study aims to explain the implementation model of digital transformation in Iranian non-governmental commercial banks. As a fundamental and qualitative study, it employs an exploratory ...
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The digital transformation of the banking sector is essential for survival and competitiveness in the digital age. This study aims to explain the implementation model of digital transformation in Iranian non-governmental commercial banks. As a fundamental and qualitative study, it employs an exploratory design to achieve its objective. The research population includes managers, consultants, specialists, and experts involved in digital transformation initiatives within these banks. Using a purposive sampling method, theoretical saturation was reached with 17 participants. Data analysis was conducted using Maxqda software. The findings, based on a paradigmatic model, revealed that causal factors such as competitive pressures, customer expectations, and market dynamics significantly influence banking's digital transformation. Contextual conditions, including environmental and economic factors, employee experience, and organizational culture, provide the foundation for implementation, while regulatory and competitive challenges act as intervening variables. These factors shape strategies and actions, such as customer-centric, operational, ecosystem, and network strategies, which lead to outcomes like enhanced customer experience, improved operational efficiency, revenue growth, greater organizational agility, and innovative business models. This model, tailored to the internal challenges and unique structure of Iran’s banking industry, offers a more flexible approach to implementing digital transformation initiatives.
Management approaches in the field of smart
hossein Mohebbi; saeid Torfi
Abstract
Artificial Intelligence (AI), as one of the most transformative emerging technologies, is rapidly evolving and reshaping various sectors. For Iran, as a developing country, understanding the future trajectories of AI and implementing strategic planning for its optimal development are of critical importance. ...
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Artificial Intelligence (AI), as one of the most transformative emerging technologies, is rapidly evolving and reshaping various sectors. For Iran, as a developing country, understanding the future trajectories of AI and implementing strategic planning for its optimal development are of critical importance. This study aims to explore the future of AI development in Iran using a structural scenario planning approach with a horizon set to 2035. The research is applied in nature and employs a descriptive-survey method for data collection. The statistical population includes university professors, managers, and experts in the AI industry. Unlike previous studies that primarily focused on specific domains of AI, this research adopts a comprehensive and national-level perspective, introducing the first structural scenario framework for AI development in Iran. It identifies and analyzes four key scenarios: the AI Vacuum, the AI Renaissance, the AI Mirage, and AI Transactions. These scenarios are built upon the analysis of critical driving forces, such as governmental policies, advanced technological infrastructure, challenges of technological singularity, geopolitical dynamics, innovation accelerators, and AI applications across industries. The findings reveal that the future development of AI in Iran is highly dependent on governmental support and the advancement of appropriate infrastructure. While critical scenarios demand immediate policy intervention, the more desirable ones offer significant opportunities for sustainable AI growth. These scenarios can serve as a foundation for designing targeted policy strategies, such as a national AI roadmap and the restructuring of innovation support systems, thereby providing a structured framework for decision-making under conditions of uncertainty.
Management approaches in the field of smart
Reza Behbood; Ehsan Bagheri; Mohamad Mahdi Zolfaghari Tehrani
Abstract
The development of augmented reality (AR) and virtual reality (VR) technologies has significantly transformed smart building projects, leading to the emergence of mixed reality (MR). This concept serves as a bridge between digital and physical worlds and is considered crucial within the smart construction ...
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The development of augmented reality (AR) and virtual reality (VR) technologies has significantly transformed smart building projects, leading to the emergence of mixed reality (MR). This concept serves as a bridge between digital and physical worlds and is considered crucial within the smart construction ecosystem. However, the adoption of MR technology among Iranian specialists is limited, with few professionals familiar with its advanced capabilities. This research investigates the perspectives of construction industry experts in Iran on the convergence of AR and VR. A mixed-method approach was employed, beginning with a review of relevant literature, followed by designing a Q-methodology survey to gather expert opinions across five specific areas. The data collected through questionnaires were analyzed using content analysis and statistical methods to identify prevailing patterns and trends. Findings revealed that integrating AR and VR can significantly enhance accuracy, reduce costs, and boost productivity. However, challenges such as technical barriers and the necessity for ongoing user training were also highlighted. The research's value lies in its comprehensive analysis of expert opinions regarding MR, which can inform optimization strategies in the construction sector. Ultimately, the study concludes that acknowledging the potential of integrated technologies, along with a focus on human and economic factors, can foster success in this evolving field.
Management approaches in the field of smart
Faezeh Zamani; Ahmad Ebrahimi; Roya Soltani; Babak Farhang Moghaddam
Abstract
This research aims to investigate the effective factors in predicting lead time (LT) and create a predictive model of LT to improve sustainability and resilience for Kanban orders in the lean supply chain (LSC). The study follows the data mining (DM) method, and the dataset includes 103023 observations ...
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This research aims to investigate the effective factors in predicting lead time (LT) and create a predictive model of LT to improve sustainability and resilience for Kanban orders in the lean supply chain (LSC). The study follows the data mining (DM) method, and the dataset includes 103023 observations from the Kanban system, which were extracted in compliance with the requirements of the dataset quality indicators in the period 1402/6 to 1402/11. First, indicators affecting the LT of orders were extracted. Process mining was used to identify influential variables in high-variance processes to improve performance and accuracy. A stepwise analysis approach was used to select features for the model fitting stage. Also, tuning the parameters of non-parametric approaches was used. The predictive model uses Multiple Linear Regression, Multiple with curvature, Lasso, Elastic Net, Boosted Decision Tree, Bootstrap Random Forest, K-Nearest Neighbor, and Boosted Multi-Layer Perceptron. The performance of the fitted regression models has been confirmed using R^2, RASE, and validation of the results and model. The results showed that the logistical features are effective in LT, and the Boosted Multi-Layer Perceptron is the best for predicting orders' LT with an accuracy of 96% and an error of 5.84. Using the model's predictive capability for new data in the Kanban system, the results obtained within four months have been used. The improvements from using DM capabilities in the Kanban system all express the significant impact of combining lean and machine learning (ML) tools to empower and resilient Lean Supply Chain Management (LSCM).IntroductionThe main problem in this research is identifying the factors that effectively predict the LT of orders in the LSC, choosing the best ML algorithm for predicting the exact LT, and how process mining can effectively identify the most repeatable variables in the main variants and investigate how DM can reduce waste in LSC.Despite classification studies on risk, disruption, and delay prediction in the literature, to our knowledge, fewer articles were found regarding the use of DM to predict the accurate LT of orders in the LSC with logistical features. Also, according to researchers, DM is considered a tool to overcome the limitations of lean tools and strengthen their performance. However, the studies corresponding to the executive case did not observe the results and improvements from the ML application in predicting the LT of orders.Therefore, in this research, in terms of innovation, 1) machine learning has been used to accurately predict the LT of Kanban orders, considering logistical factors, 2) Process mining has been used in the identification stage of influential variables, 3) The results and improvements obtained from predicting the LT of orders regarding risk reduction and sustainability improvement have been examined and compared.Research Question(s)The main questions in this research are specified as follows:What factors affect LT's prediction in the lean supply chain?How do we predict the LT in the lean supply chain?How can DM effectively reduce waste in the lean supply chain?Literature ReviewRegarding the issue's importance and urgency, transparency and accurate prediction of the LT have reduced risk and improved sustainability and resilience in the LSC. These effects are significant in both theoretical and operational dimensions, such as reducing logistic costs, safety stock, working capital, stoppage, level of inventories, storage cost, energy consumption, and risk. After reviewing the literature, the most relevant articles in the field of ML are listed in Table2.MethodologyThis research is practical from the objective point of view, and from the data point of view, it is quantitative. This study includes four main processes: 1) reviewing the literature and data collection, 2) research method and pre-processing, 3) model construction, and 4) model evaluation and results (Jayanti, 2022 & Wasesa). First, influential variables were extracted by reviewing the literature. Then, the dataset was extracted from the Kanban system in compliance with the requirements of the data set quality indicators from 6/1402 to 11/1402. Then, process mining was used to identify the features with the most repeatability in the main variants, and finally, influential variables were extracted through brainstorming. An integrated stepwise analysis approach has been used to select features. The predictive model uses MLR, curvature, Lasso, Elastic Net, Boosted DT, Bootstrap RF, KNN, and Boosted Multi-Layer Perceptron. The parameters of non-parametric approaches are tuned to improve forecasting performance and accuracy. In this research, evaluation and validation are the main criteria for evaluating the model's predictive power, and error and accuracy indices have been used together. Therefore, the performance of the fitted regression models using R^2 and RASE evaluation indices and validation of the results and the model are confirmed.ResultsAfter fitting the regression models, for each row of test data, predict the LT and compare it with the actual values of the LT; then, to identify the best model, R^2, RASE, and model comparison approaches are used.The results show that the Boosted Multi-Layer Perceptron, with one hidden layer, five activation functions, and a learning rate of 0.1, has the highest accuracy at 96% and the lowest root average square error at 5.84, compared to other fitted models.Discussion and ConclusionThe obtained results show that the identified independent variables are related to customer factors (safety stock), manufacturer factors (inspection status, quality paint), logistic factors (vehicle, distance), part factors (name, part-expert), and order factors (number of holidays, Kanban issue date) are effective on the LT. As the selected model in this research, the regression model of the Boosted Multi-Layer Perceptron has the highest R^2 and the lowest RASE criteria. Process mining is practical and helpful in identifying the main variants. By using the model's predictive capability for new data in the Kanban order issuing system within four months, the improvements all express the significant impact of combining lean tools and ML to empower LSCM. The practical implications of this research can guide managers in implementing practices with lean tools, improving sustainability, eliminating waste, and being more competitive in the current challenging business environment. Academics can benefit from the present study because it provides ML practices that can be further tested and validated.This research generalizes and develops the use of DM as a decision-making support tool in predicting the LT to overcome the limitations of lean tools, and it can improve the efficiency and stability of the LSC and reduce the risk. While this research provides valuable insights, it also has limitations, including the lack of data on influential variables identified in the literature. In implementing this research, there are suggestions for future research that examine factors such as production capacity, weather, and location conditions and deep learning to fit more reliable and accurate results and investigate prescriptive analyses to optimize the LT of orders based on the fitted regression models, the design of the experiment and using the profiler's capabilities.Keywords: Machine Learning, Regression, Lean Supply Chain Management, Kanban, Lead Time.
Management approaches in the field of smart
seyed Mohsen Safavi koohsareh; seyed amin hosseini sano; Amirhossein Mohajerzadeh
Abstract
The primary objective of mobile network operators is arguably to maximize their efficiency. Beyond operational and investment costs, maximizing the utilization of available resources can help them achieve this goal. To this end, operators offer discounted data plans during off-peak hours to encourage ...
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The primary objective of mobile network operators is arguably to maximize their efficiency. Beyond operational and investment costs, maximizing the utilization of available resources can help them achieve this goal. To this end, operators offer discounted data plans during off-peak hours to encourage users to utilize the network during these times. These data plans are typically based on the average traffic load across the entire network at different times of the day. However, they often overlook the fact that traffic patterns can vary significantly across different population areas within a city at various times. In this paper, different population areas are automatically identified using clustering based on traffic patterns. By identifying these areas and considering the traffic patterns specific to each area, the allocation of appropriate data plans for users, based on the regions they frequent, is analyzed and discussed. Additionally, other potential applications of this clustering method for offering various services are presented, followed by a conclusion.IntroductionThe number of cellular network users and their required bandwidth are continuously increasing (Ericsson, 2022). However, limited wireless frequency bands constrain network capacity, prompting operators to deploy dense base stations to reuse radio frequencies in smaller coverage areas, thereby enhancing capacity. Operators plan for peak usage, leading to base station layouts that often remain underutilized for extended periods, resulting in inefficient use of capital (equipment) and operational (energy and maintenance) costs (Liu et al., 2023). To address this, operators offer discounted plans during low-traffic periods but overlook the varying traffic patterns across urban areas, which could enable tailored offers for different regions. This paper proposes a hierarchical clustering-based method to identify and segment urban areas, design region-specific traffic-based plans, and target appropriate users. The main contribution is improving efficiency by maximizing the utilization of existing cellular networks without expanding capacity, benefiting both operators through increased revenue and users through enhanced satisfaction.MethodologyThe best approach to evaluate proposed solutions in cellular networks is to use real-world datasets from mobile operators. Cellular network logs are vast, contain sensitive user and network information, and require algorithms capable of handling large-scale data. In this study, we use a publicly available dataset (Barlacchi et al., 2015) containing telecommunication, weather, news, social media, and power grid data from Milan and Trentino, Italy, spanning November 1, 2013, to June 1, 2014. Our focus is on telecommunication data, specifically Call Detail Records (CDRs), to evaluate the proposed method.The dataset is processed and analyzed using Python and libraries such as NumPy, Pandas, Scikit-learn, and Matplotlib. The proposed method involves clustering base stations based on traffic patterns, designing region-specific data plans, and targeting users during low-traffic periods.3.1. Traffic Pattern-Based Region IdentificationAs mentioned earlier, traffic patterns of cellular base stations vary across urban areas. These patterns are heavily influenced by the stations' locations. For example, base stations in residential areas exhibit different traffic patterns compared to those in commercial, transportation, or recreational zones (Xu et al., 2017).Figure 1: Traffic Patterns of Base Stations in Three Different Population Zones Figure 1 illustrates the traffic patterns of base stations in three different population zones over a week. Zone 3 likely corresponds to recreational areas like amusement parks, with higher traffic on weekends. Zone 1 may represent office areas, with reduced traffic on weekends, while Zone 2 could be industrial or transit areas with consistent traffic throughout the week.To separate these zones, hierarchical clustering is employed (Abubakar et al., 2022). Instead of using Euclidean distance, which fails to distinguish adjacent zones with different traffic patterns, we use traffic time series as the clustering criterion. The chosen algorithm is agglomerative hierarchical clustering (Kassambara, 2017), as shown in Figure 2. Base stations first remove noise from their data and send average traffic data to a central node every x minutes. At the central node, Euclidean distance is used to measure traffic similarity between stations, reducing dimensionality from two dimensions (time series traffic volume) to one (distance between clusters). Over 80% of time series similarity studies use this metric, though some employ deep learning for feature extraction to improve clustering.The Euclidean distance between two base stations' traffic time series Q and C is calculated as:1 To mitigate sensitivity to variations, preprocessing steps include removing outliers, adjusting offsets, and smoothing noise using moving averages (Keogh & Pazzani, 1998).The hierarchical clustering dendrogram (Figure 2) determines the optimal number of clusters by identifying the best cut-off line. Two strategies are proposed:Predefine the number of clusters based on comprehensive traffic pattern analysis and use k-means clustering.Use silhouette scoring to dynamically determine the optimal number of clusters based on traffic similarity.We adopt the second approach, using average silhouette scores (Almeida et al., 2015) to select the optimal number of clusters. This method eliminates the need for predefined cluster counts and provides precise cluster identification.Once clusters are identified, data plans are designed for each cluster based on their traffic patterns.3.2. Designing Data PlansFor each cluster, the average traffic profile is calculated, and data plans are designed inversely proportional to traffic volume. The number of offers q in time interval t is determined by:2 where A and B are the traffic range bounds, S is the current traffic, N is the maximum number of offers, and C is the minimum (0).Alternative models, such as linear, exponential growth/decay, and logarithmic growth/decay, are also explored (Safavi et al., 2024), as shown in Figures 5 and 6.3.3. Targeting UsersUsers with higher overlap with low-traffic periods are prioritized for data plan offers. A user’s average monthly presence in low-traffic intervals is used to rank them. The longest data plans are assigned to users with the highest presence in low-traffic periods, ensuring efficient resource allocation.ResultsSimulations represents the method proposed in this paper, utilize 100% of the network's bandwidth capacity.results demonstrate the optimal utilization of existing equipment and resources, which directly correlates with increased operator profitability. Those also show that the proposed method can maximize resource efficiency by approximately 40%, representing the highest possible improvement in network resource utilizationWe can conclude, the proposed method significantly enhances resource utilization and operator profitability by fully leveraging network capacity. While other scenarios improve resource usage to some extent, only the proposed method achieves 100% utilization, highlighting its effectiveness in optimizing network performance and operational efficiency.Keywords: Mobile Network Operator, Maximizing the Utilization, Cellular Data Plan, Clustering, Traffic Pattern.
Management approaches in the field of smart
Mohammad Taghi Taghavifard; Payam Hanafizadeh; Saeedeh Mehri; Iman Raeesi Vanani
Abstract
Business model change in startups is essential for adapting to evolving market demands and increasing competitiveness, playing a critical role in their success. However, most research related to business model change has mainly focused on established firms. To address this research gap, the present study ...
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Business model change in startups is essential for adapting to evolving market demands and increasing competitiveness, playing a critical role in their success. However, most research related to business model change has mainly focused on established firms. To address this research gap, the present study provides a framework that contributes to understanding the role of design themes in startup business model changes. In this study, a systematic literature review was used to analyze theoretical foundations and prior researches. After defining the research question, designing a search strategy, and applying screening criteria, 95 articles published between 2000 and 2024 were reviewed. In the first stage, a conceptual model was developed to analyze the dimensions of business model change by reviewing theoretical foundations and prior researches. This model was then refined through inductive-deductive thematic analysis, using evidence from empirical articles and case studies. The identified themes—novelty, efficiency, lock-in, complementarity, and adaptability—were examined across four main dimensions of business model change: content, structure, governance, and stream. The findings showed these themes interact synergistically and contribute to competitive advantage and business sustainability. The research results suggest that these five overarching themes provide a suitable framework for understanding and managing business model change in startups.IntroductionIn recent years, the topic of entrepreneurship and startups has attracted significant attention and has emerged as a key driver of economic growth. The business model of startups plays a fundamental role in this process, serving as a mechanism for exploiting entrepreneurial opportunities and creating value (Amit & Zott, 2001; George & Bock, 2011; Guo et al., 2020). However, one of the key characteristics of the startup and entrepreneurial environment is high uncertainty, which static business models are unable to effectively adress (Demil & Lecocq, 2010). Accordingly, the main objective of this research is to develop a framework for changing the business models of digital startups and to offer practical insights for entrepreneurs. To explore the dynamics of startup business model change, this study addresses the following key research question:Research QuestionHow do startup business models evolve, and which business model themes drive this change?To answer this question, a conceptual framework was developed based on the business model structure proposed by Amit and Zott (2001) and the business model innovation framework proposed by Spieth and Schneider (2016). Literature ReviewThe existing literature on business model change follows two major approaches: the evolutionary approach and the theme-based approach (Guo et al., 2020).2.1. Evolutionary Approach to Business ModelsFrom an evolutionary approach, the business model of the startup changes and evolves in a dynamic manner through flexibility (Bock et al., 2012), experimentation (Andries et al., 2013), and trial-and-error learning (Chesbrough, 2010; Sosna et al., 2010). This approach draws upon methodologies such as the Lean Startup (Ries, 2011) and Customer Development (Blank, 2013), which suggest that business models evolve through iterative testing and customer feedback. Studies based on this approach show that incremental and continuous changes in business models require careful attention to the firm's internal resources and competencies, as explained by the resource-based view (RBV) and dynamic capabilities theory (Schneider & Spieth, 2013).2.2. Theme-Based Approach to Business ModelsThe theme-based approach focuses on using specific themes to structure business models and value creation. The four conventional themes are novelty, efficiency, lock-in, and complementarity (Amit & Zott, 2001, 2012; Kulins et al., 2016; Pati et al., 2018):Novelty: Refers to innovation in products or services or underlying resources and capabilities.Efficiency: Focuses on cost optimization and resource utilization.Lock-in: Helps strengthen long-term relationships with customers and partners.Complementarity: Enhances synergies among different value propositions.Recent studies (Zott & Amit, 2007; Ojala, 2016; Costa & Haftor, 2021) have demonstrated the effectiveness of this approach in fostering strategic entrepreneurship, allowing startups to integrate external opportunities with internal capabilities for sustained growth.2.3. Research Gap and Initial Conceptual ModelBusiness model change research has primarily focused on established firms (Achtenhagen et al., 2013; Bohnsack et al., 2014), while business model change in startups - a broadly defined type of organization with limited resources, high uncertainty, flexibility, and differences in value creation sources - has been less examined in the academic literature (Kesting & Günzel-Jensen, 2015; Guckenbiehl & Corral de Zubielqui, 2022). Amit and Zott (2001) recommend more research on the dynamics of business model themes, a gap reiterated by Randhawa et al. (2020). In this study, based on the business model structure by Amit and Zott (2001) and the stream dimension of Spieth and Schneider (2016), we analyze startup business model change themes across four dimensions: content, structure, governance, and stream.MethodologyThis study adopts an inductive–deductive approach and conducts a systematic literature review on startup business model change. The review process adhered to Tranfield et al.'s (2003) framework: planning the review; conducting the review; analyzing the findings. The articles included in the literature review were from articles published between 2000–2024 and they were retrieved from the Scopus and Web of Science databases. Initial output of 197 articles were ultimately reduced to 95 articles for in-depth review after duplicates and irrelevant articles were removed. To identify core concepts and themes related to business model change, the data were coded and analyzed using ATLAS.ti software.ResultsThematic analysis using the theme network tool led to the identification of five primary themes: novelty, efficiency, lock-in, complementarity, and adaptability. Each of these themes represents business model change in four dimensions: content, structure, governance, and stream (see Table 1) Table 1. Final Framework of Startup Business Model Change Based on Business Model Design ThemesDimensions NoveltyEfficiencyLock-InComplementarityAdaptabilityContent Products, services, information, or value propositionsüüüüüResources and assetsüü üüCapabilities and competenciesü üüStructure Customer segments and their relationsü üParticipants and their relationsü üüCommunication mechanismsüüüüüGovernance Controllersü ü Formal and legal structure üIncentivesü ü Stream Revenue streamsü üüCost structuresüü üInvestment structuresü üDiscussionThe framework proposed in this study introduces an additional dimension—stream—which extends the three-dimensional concep outlined by Amit and Zott (2001): content, structure, and governance. Furthermore, the inclusion of adaptability, which is not present in Amit and Zott’s model, is a significant innovation. Supported by Sharma et al.'s (2016) model, this framework integrates adaptability into a unified business model design, addressing dynamic environments and emerging market challenges beyond operational management.ConclusionThis study offers two important contributions to the literature by providing a new conceptual framework for startup business model change (Table 1). First, in addition to the four conventional business model themes (novelty, efficiency, lock-in, complementarity), the adaptability theme is introduced to demonstrate the importance of adaptability in changing and evolving business models. Second, this study provides new insights into how business models change by adding the stream alongside the content, structure, and governance dimensions.Keywords: Startup, Business Model Change, Business Model Design Themes.
Management approaches in the field of smart
Fahime Mahavarpour; Feiz Davood; Morteza Maleki Min Bash Razgah
Abstract
Augmented reality technology has emerged as one of the main trends in the digital market in recent years. This new technology has been successively used in innovative businesses due to its attractiveness and potential. The aim of the current research was a systematic and comprehensive review ...
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Augmented reality technology has emerged as one of the main trends in the digital market in recent years. This new technology has been successively used in innovative businesses due to its attractiveness and potential. The aim of the current research was a systematic and comprehensive review in the form of a bibliometric approach on the citation data of the purchase decision-making literature based on augmented reality technology. A pragmatic paradigm allowed the researcher to collect citation data from the Scopus database in a systematic strategy and preprocess it in the form of the standard Prism protocol. Finally, 239 studies were included in the bibliometric analysis basket by R Studio and Vosviewer software. The final results in the functional section identified the most influential documents, authors, journals, organizations, and countries, and then added highlights to buyer behavior and purchase decision-making with augmented reality technology in virtual businesses in the form of interaction patterns between elements and data content analysis.. It is also worth noting that this concept mainly revolves around five main areas: the virtual and psychological experiences of shoppers in online shopping with augmented reality; The effects of artificial intelligence on new technologies in the behavior of buyers; virtual and psychological experiences of buyers in online shopping with augmented reality; Interactions of technology, perceived value and cognition on shoppers' experience in retail using mobile augmented reality; The role of augmented reality technology in purchasing decisions and smart purchases in virtual space. The influential school of augmented reality technology in buyers' decision-making is related to the stimulus-organism-response theory, which formed the intellectual foundation of this field.
Introduction
Try to conduct marketing without using augmented reality technology; you will not succeed. Try to create an inspiring marketing strategy without augmented reality technology; this will not be effective either. While traditional marketing is effective, it involves high costs and difficulties in engaging and providing personalized services to customers, as it cannot adjust services to recognize customers' needs or expectations. Marketing with this novel technology (AR) represents a new and potentially impactful subfield that has transformed how buyers experience goods and services. Augmented reality aids in the development of marketing, offering benefits that enhance the company's image, strengthen customer interaction, and increase sales. These advanced new technologies have already positively boosted marketing. AR technology is in its early stages and there are ample opportunities for its improvement. This innovative technology (AR) provides an excellent option for enriching perceptual and interactive desires. While many established consumer behavior theories may extend naturally into virtual spaces, many of them may require significant updates to align with buyers' search, selection, and usage practices. During purchase decision-making, whether interacting with physical or online stores, buyers encounter various touchpoints that determine the shopping experience. Academic knowledge is expanding exponentially. This has made it challenging to keep up with the latest innovations in research and evaluate the collective evidence in a particular field of study. One such database is Scopus, created in 2004 and published by Elsevier. Managing such knowledge using traditional tools and techniques is difficult, if not unlikely, due to the rapid growth. This is why literature review as a research method has become more popular than ever in various natural and social science disciplines. The purpose of a systematic review is to identify all empirical evidence that meets pre-specified criteria to answer a specific research question or hypothesis. Bibliometrics is a new style of theoretical literature review and a type of systematic review that has created related theories and bibliometric analysis in various fields of knowledge, including management sciences. The value of bibliometric methods lies in their ability to analyze the evolution of scientific literature over time and reveal intellectual relationships in this field. Therefore, based on the present research problem, which seeks to describe performance indicators, examine the interactive patterns of these indicators, and analyze the content of citation information in the field of augmented reality technology in the academic space, the researcher aims to demonstrate the study gap in this field and contribute to filling the knowledge gap. In the introduction section, a systematic methodology is first defined to specify the steps for collecting, preprocessing, and analyzing information. Then, in the results section, an objective and tangible interpretation is provided using tables and charts from professional bibliometric software. Finally, the obtained results are discussed and concluded within the context of expanding the present literature.
Methodology
Systematic reviews indicate a precise approach to integrating and evaluating scientific evidence literature reviews are no exception and are guided by a systematic process. This process aids researchers in systematically and comprehensively gathering, analyzing, and evaluating existing studies, providing an overall picture of the state and trends within a scientific field. Compared to systematic literature reviews, this method helps prevent author bias. The present bibliometric methodology emerges from a pragmatic paradigm that designs various stages of research based on common assumptions and beliefs among review researchers Based on the outputs of scientific research, the researcher has the freedom to use various quantitative and qualitative approaches within this philosophical framework. According to bibliometric studies, the methodology of bibliometric research consists of five steps, which are detailed below.
Chart 3: The Methodological Process of Bibliometric Studies (Moradi & Miralmasi, 2020b, p. 570)
Results
In this study, the focus is on the topic of augmented reality technology and its evolution up to the year 2024. While augmented reality technology saw erratic growth in buyer behavior and purchase decision-making during its nascent stage up to 2015, this was largely due to the limited understanding of its long-term effects owing to the lack of metrics, measurable elements, and research studies at that time.
There is a 50-year span of articles, indicating over half a century of discussion surrounding augmented reality technology. However, from 2015 onwards, it has experienced an annual growth rate of 25.92%, showing an upward trend.
Approximately 36.46% of the research has been authored through international collaborations, as this field requires expertise from various research disciplines.
Developing countries, despite having fewer scientific outputs, publish their articles as international collaborative studies to ensure publication in reputable journals and to increase citations to their studies.
Keywords: Augmented Reality Technology, Marketing, Buyer Decision Making, Buyer Behavior, Virtual Businesses.
Management approaches in the field of smart
Homa Soufi; Habib Roodsaz; Davoud Hosseinpour; Hosein Aslipour
Abstract
Digital transformation in the banking sector is recognized as a critical driver of growth and change. This study investigates the anticipated outcomes (both outputs and impacts) following the implementation of digital banking policies, utilizing an exploratory and applied-developmental approach. The ...
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Digital transformation in the banking sector is recognized as a critical driver of growth and change. This study investigates the anticipated outcomes (both outputs and impacts) following the implementation of digital banking policies, utilizing an exploratory and applied-developmental approach. The research adopts a mixed-method design, incorporating both qualitative and quantitative phases. In the qualitative phase, experts in banking and policymaking were interviewed using purposive sampling, reaching theoretical saturation after 25 interviews. In the quantitative phase, data was collected from 354 bank managers and professionals through a researcher-developed questionnaire. The data was analyzed using thematic analysis for the qualitative part and the Partial Least Squares (PLS) method for the quantitative part. The results indicate that the initial outcomes of digital banking policy implementation include improvements in revenue and market positioning, cost efficiency, customer acquisition and satisfaction, bank infrastructure, data management, banking products and services, banking technologies and channels, and risk management. Additionally, the long-term and sustainable impacts of digital transformation in the banking system encompass transparency and justice, the creation of new opportunities, the future outlook of banking and the economy, digital leadership and mindset, social and environmental impacts, long-term policymaking and planning, as well as internal and external networking.
Introduction
Digital transformation is a crucial driver of development, particularly in the banking sector, where it reshapes traditional business models and operational processes, enabling efficient online financial services. Despite these advancements, Iranian banks face infrastructural and regulatory challenges, such as weak IT systems and traditional organizational cultures, which hinder the full adoption of digital solutions. To address these issues and align with global standards, comprehensive digital banking policies are necessary. This study explores the effects of these policies, focusing on the changes they bring about in both the short and long term. In this context, the fundamental research question is: What are the expected dimensions and components of the outputs and long-term impacts following the implementation of digital banking policies during the evaluation period?
Literature Review
Digital transformation in banking refers to the use of digital technologies to fundamentally improve and change the processes and structures of traditional banking. This transformation includes the application of technologies such as artificial intelligence, blockchain, big data, and cloud computing, which enable banks to offer innovative and customized services to customers (Vial, 2014). The emergence of fintech companies and the provision of digital financial services have pressured traditional banks to digitalize their structures. As a result, banks have developed digital transformation policies to remain competitive and align with market changes (Salamatitaba et al., 2017). In digital banking policy-making, evaluation is recognized as one of the most critical stages in the policy cycle. According to various literature and definitions, outputs are the immediate results obtained in the short term following the implementation of the policy, including direct improvements in digital banking services and processes. These results are typically observable within 1 to 3 years after the policy is implemented. In contrast, impacts refer to the long-term outcomes that emerge between 5 to 10 years post-implementation, encompassing the lasting effects and consequences of the policy. Research indicates that digital transformation in banking leads to improved customer satisfaction, enhanced transaction security, and optimized processes.
Methodology
This applied-developmental study employs a mixed-method approach. In the qualitative phase, 31 banking and policy experts were selected through purposive and snowball sampling, with saturation reached after 25 interviews. Data were analyzed using thematic analysis with MAXQDA software. In the quantitative phase, a survey was administered to 354 bank managers and experts, with the sample size calculated using Cochran's formula. The researcher-developed questionnaire included 36 items based on a five-point Likert scale. Reliability was evaluated using Cronbach’s alpha, yielding a score above 0.7. Data analysis was conducted using the Partial Least Squares (PLS) method with SmartPLS software to validate the findings.
Results
The thematic analysis of the qualitative data identified 88 descriptive codes, 21 interpretative codes, and 8 overarching themes for the outputs, as well as 54 descriptive codes, 15 interpretative codes, and 7 overarching themes for the impacts following the implementation of digital banking policies. The outputs were categorized into areas such as revenue and market positioning, cost efficiency, customer acquisition and satisfaction, bank infrastructure, data management, products and services, banking technologies and channels, and risk management. The long-term impacts included dimensions such as transparency and justice, the creation of new opportunities, the outlook for banking and the economy, digital thinking and leadership, social and environmental impacts, long-term policymaking and planning, and internal and external networking. Quantitative data analysis was conducted using the Partial Least Squares (PLS) method with SmartPLS software. The model was validated through relevant statistical tests, demonstrating a satisfactory fit and reliability.
Discussion & Conclusion
The findings align with key studies. Czerwińska et al. (2021) confirmed that investment in digital technologies enhances banks' competitive positions, which this study supports. Similarly, Lydiana et al. (2022) highlighted the role of digital transformation in fostering innovation, with this study further expanding the focus to service development. Consistent with Agboola et al. (2019), this research shows that digital transformation improves cost efficiency and operational performance. The impact on customer satisfaction and acquisition aligns with Aydin & Onayli (2020), emphasizing the role of digital experiences in customer growth. The analysis of organizational transformation matches Mirković et al. (2019), and the emphasis on data management is consistent with Sadigh et al (2022).
This study differentiates between the short-term outputs and long-term impacts of digital transformation in banks, providing a unique perspective compared to previous studies. It offers localized insights tailored to the Iranian market, making the findings particularly relevant for policymakers. It emphasizes that policymakers and regulatory bodies, such as the Central Bank, should prioritize strengthening IT infrastructure, developing comprehensive data security and privacy regulations, and focusing on new technologies like open banking and big data. The results indicate that the long-term effects of digital transformation on economic indicators and customer behavior require further investigation. Future studies could explore these effects over different time periods and examine how technologies such as AI and blockchain influence customer psychology, including decision-making, trust, and satisfaction, as well as social factors like access to banking services and the distribution of opportunities.
Keywords: Digital Transformation, Digital Banking, Policy Evaluation, Policy Outcomes.
پسآیندهای خطمشی بانکداری دیجیتال
Management approaches in the field of smart
Aryan Setareh Tabrizi; Ali Mohtashami
Abstract
Shipping industries in ports, as one of the main and strategic industries, have always performed an important role in the optimized management of the transportation business in a country. Moreover, it has an impact on the country's economy. The future status of port’s business management can be ...
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Shipping industries in ports, as one of the main and strategic industries, have always performed an important role in the optimized management of the transportation business in a country. Moreover, it has an impact on the country's economy. The future status of port’s business management can be seen in planning and forecasting, access to new technology in order to smartness, access to logistics, increase in specialized human resources and the impact of ports globalization. For this reason, the ports have to provide their services in a sutaible way in order to plan for their business development and supply demands. Also, the process of these changes in the future will be more than that, it will have a direct impact on the technology and facilities of the port. According to the mentioned points, in this article, in order to promote the ports and shipping industry, in the first step, the main indicators of green and smart ports are identified by library study, and then interviews are conducted with thematic analysis system approach. Next, the conceptual model of the research was drawn.Consequently, each index in the drawn pattern and the final index are evaluated in MATLAB software by Fuzzy Inference. As result of that, it is possible to determine the smartness and environmental indicators in the ports of the country for the first time. In this regard, Anzali port, one of the country's most important ports, will be implemented and concluded as a real example.
Introduction
Ports and harbors face serious competition to deliver a more efficient and safer flow of goods around the world. In this context, a new concept has emerged, which is called a smart port (2019, Molavi). Targeted smart port initiatives seek to remove specific barriers at ports. These initiatives are largely focused on specific applications of information and communication technology and regulation-based approaches in smart ports. These rules are aimed at improving port sustainability, the implementation of new technologies and providing port performance evaluation standards. In this part, the generalities of the research are presented in the form of stating the problem and the necessity of conducting the research, and in the next part, the goals and assumptions of the research are presented. Finally, the executive structure of research and innovation is presented.
Research Methodology
In this article, two library and field methods have been used to collect information. We reviewed educational theses, foreign and Iranian published books, Persian and English publications and some textbooks. Then, the basis the questions for the interview was designed. In this research, the snowball method was used and the number of experts in this field reached fifty people. Then, the subject method is used to obtain the data and information needed to identify the indicators. Semi-structured interviews were used based on the interview protocol. (2019, Yarahmad Ghasemi)
Innovation and novelty of the research:
Background and up-to-date information on research innovation:
Research Port 360 has recently prepared a comprehensive report in collaboration with the World Ports Association which report aims to examine the activities related to the management of smart port markets in the period 2023-2028 and is intended to be used in the agenda of all ports in the world.
Through the analysis and research conducted in this report, the dynamic chain of the global smart ports industry market during the period 2023-2028 has been well studied and an overview of how ports are becoming smart has been provided.
In the regulation of technology, it can be said that a digital port describes a connected port that uses broadband communication infrastructure, flexible and service-oriented computing infrastructure, and innovative services to meet demands. On the other hand, intelligence, along with international laws and regulations in the field of environment, results in a developed port.
Some of the things that distinguish this research from other research in terms of innovation are as follows:
In this research, services in the field of ports are considered in both implementation forms combined with a strategy and which have been thoroughly examined in research interviews, which has not been seen in the articles in this way as it has been accurately displayed in the maximum research table.
In this research, all four service levels that should be considered in ports, such as traffic management, safety, environment and ship management, have been examined in the research to extract concepts and ultimately the main indicators.
The managerial and executive application of this research is ultimately the implementation of environmental and smart components in the country's ports, which leads to the sustainable development of ports.
Research findings
According to the thematic analysis, the smartness and greenness of ports is drawn in Figure 4. We should evaluate all the indicators obtained from the evaluation model with the Mamdani logic in FIS with MATLAB software in order to determine the level of implementation of the intelligence and environmental model of ports. The inputs are: 1) port intelligence 2) greenhouse gas production 3) smart infrastructure 4) water and waste management 5) renewable resources utilization 6) environmental management 7) cultural support 8) energy consumption 9) safety management 10) greenness of the ports. Considering the average value for all the inputs, we will see that the result for the output is equal to the average. For instance, according to Figure 9, the environmental model and intelligence in the areas where the value of the smart technology is very high and high, and the amount of the greenhouse gas production is low and very low, the result for the environmental and intelligence model is high. After that, Anzali port, was considered as a real model and the results is obtained as Figure 12. It indicates that the organization of this port is not ready to implement the designed model.
Conclusions and Discussion
Evaluating the efficiency of ports in terms of compliance with green and intelligence indicators is very important and strategic. (Tabrizi, 2023) In this study, first the main indicators for evaluating green and smart ports were identified through a library study and a thematic analysis system, and then the conceptual model of the research was drawn. Each indicator was evaluated based on the fuzzy inference, and finally all indicators were embedded in the form of a final model in FIS, which can be implemented for the first time in the country's port business. In this regard, it was implemented in Anzali Port as a real example and conclusions were drawn. It is necessary that the issue of greenness or the environment of ports be given priority attention by ports of countries, because for international communications especially European ports, the greenness of ports is of great importance. The issue of greenness of ports is put on the agenda, and the need to implement smart indicators in ports requires more attention, because in analyzing the indicators by fuzzy inference and also implementing the model on the real example, it shows that the smartness component is given little importance in the country's port business, which is hoped to be resolved separately. Therefore, evaluating the efficiency of ports in terms of compliance with green and intelligence indicators is very important and strategic.
Keywords: Transit Ports, Intelligent Factors of Port Business Management, Green Factor, Thematic Analysis, Fuzzy Inference.
Management approaches in the field of smart
Elnaz Valizadeh Hamzekolaei; Ameneh khadivar; Fatemeh Abbasi
Abstract
Abstract
Social networks have become vital for sharing opinions and feelings through user-generated content. Many organizations leverage analytics to enhance decision-making, yet most sentiment analysis studies focus on commercial businesses, neglecting non-profits despite their significant social media ...
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Abstract
Social networks have become vital for sharing opinions and feelings through user-generated content. Many organizations leverage analytics to enhance decision-making, yet most sentiment analysis studies focus on commercial businesses, neglecting non-profits despite their significant social media presence. This research investigates the impact of user-generated content on the financial performance of non-profit organizations using a dataset of 26,714 tweets from 23 accounts. Results indicate that while positive emotions do not affect financial performance, negative emotions and retweets harm it, whereas likes positively influence revenue. Future research should explore additional social networks and broader data collection methods.
Introduction
Free market societies comprise three main sectors: the public sector, the private business sector, and private non-profit organizations, collectively known as the "three-sector economy" (Weisbrod, 1975). Non-profit organizations are characterized by being organized, private, self-managed, non-profit distributing, and voluntary (Salamon et al., 1996). This research focuses on non-profits dedicated to animal welfare, which aims to prevent animal abuse and ensure proper care (Navigator Charity, n.d.). Recently, these organizations have gained prominence and significantly influenced societal modernization (Lee & Nowell, 2014). Evaluating their performance is crucial for enhancing efficiency amid increasing competition for funding. Given the challenges of measuring performance in non-profits, this study employs sentiment analysis of user-generated content on Twitter to assess organizational effectiveness.
Research Question(s)
How is the performance of non-profit organizations evaluated using user opinion analysis?
Literature Review
This literature review examines existing studies relevant to the research topic and identifies gaps that necessitate this investigation. Non-profit organizations generate revenue and publish annual financial statements (Rathi et al., 2016). They increasingly use social networks to engage with stakeholders (Lai et al., 2017), producing content that can yield valuable insights through user opinion analysis (Miller, 2011). Social networks enable these organizations to gather stakeholder feedback, enhancing decision-making (Waters & Lo, 2012). Non-profits typically focus on measuring performance through donor revenue and budget progress, emphasizing the importance of both financial and non-financial metrics (Epstein & McFarlan, 2011; Kaplan, 2001).
Research has explored the effects of user-generated content on the performance of both non-profit and for-profit organizations. For instance, studies have shown that Twitter content can predict sales for commercial enterprises (Liu et al., 2016) and that negative user messages elicit more responses, prompting businesses to adapt their communication strategies (Hewett et al., 2016). Additionally, analysis of user content has identified key factors influencing millennial engagement online (Saura et al., 2019). Another study linked customer feedback on Twitter to satisfaction and dissatisfaction factors in the hotel industry (Xu et al., 2017), while research demonstrated that emotions in text comments significantly affect product sales performance (Li, 2018).
In non-profit contexts, stakeholder-generated content can enhance participation strategies (Saxton & Waters, 2014), and increased social campaign popularity correlates with heightened discussion (Tayal & Yadav, 2016). However, most existing research focuses on emotional impacts on specific campaigns rather than overall organizational performance, revealing a significant gap. This study aims to fill this gap by evaluating non-profit performance through sentiment analysis of social media content, presenting an innovative model that incorporates econometric methods. Thus, this research represents a novel contribution to understanding non-profit performance evaluation.
Methodology
This study examines selected non-profit organizations evaluated through "Charity Navigator," a prominent charity evaluator in the United States. To refine our sample and focus on larger, more active organizations on social media, we began with a pool of 9,000 non-profits and used advanced search filters (see Table 1) to identify 60 organizations. From this group, five organizations were randomly chosen for further analysis.
Table 1. Title of filters for advanced search of non-profit organizations on charitynavigator website
The title of the characteristics of non-profit organizations
Characteristics of non-profit organizations
Social network
Twitter
Select category-type
Place
Income
Site ranking
Work area
Animals - rights, welfare and services to animals
The entire United States of America
No restrictions
No restrictions
international
Data collection involved manually extracting financial reports detailing total income for each organization from 2010 to 2020 via the "ProPublica" website. We also identified 23 English-language Twitter accounts related to these organizations, from which we gathered tweet data using a Python-based web crawler.
Data preprocessing included removing non-English texts, hashtags, mentions, URLs, punctuation, and stop words, as well as performing tokenization and lemmatization. This resulted in a dataset of 22,829 tweets from the five selected non-profits.
Data Visualization
Word Cloud: We generated a word cloud using the hyperwords package in Python, displaying the 50 most frequently used words, with their size reflecting usage frequency.
Topic Modeling: To explore underlying topics in the tweets, we applied Dirichlet’s hidden allocation algorithm, identifying five main themes:
Vegetarian education
Addressing cruelty and rescuing animals
Animal protection
Monitoring organizational actions
Supporting organizational activities
Results showed vegetarian education was the most discussed topic, while support for non-profits was the least frequent, indicating users prioritize other issues.
Sentiment Analysis
We conducted sentiment analysis using a vocabulary-based approach. Texts were standardized to lowercase, and stop words and punctuation were removed. A dataset of 1,000 tagged examples was used to evaluate sentiment accuracy.
Econometric Analysis
This research assesses how user-generated content on Twitter impacts the annual income of non-profits. After necessary tests and model estimation using Evioz 10 software, we evaluated the effects of independent variables on total revenue, summarized in Table 2.
Table 2. Variables used in the model
Mathematical symbol
English symbol
Mean
1
y
Total Revenue
Total Revenue
X1
X2
X3
X4
X5
Volume
Neg
Positive
Retweet
Favorite
Volume of tweets
Negative sentiments
Positive sentiments
Number of retweets
Number of favorits
The regression model is expressed as:Total Revenue = αi + β1X1it + β2X2it + β3X3it + β4X4it + β5X5it + eit
We conducted Chow or Flimer tests to determine data structure and used Fisher’s test for the unit root test, confirming stationarity at a 95% confidence level.
Model Estimation
Using the OLS method, we found the model significant at the 95% confidence level, with a coefficient of determination of 0.512638, indicating that over half of the variability in total revenue is explained by the model. The Durbin-Watson statistic indicated no autocorrelation among residuals.
Results showed a significant relationship between Twitter content and financial performance. Negative sentiments and retweets inversely affected financial outcomes, while likes positively correlated with revenue, indicating active support from followers. No significant relationship was found between tweet volume or positive sentiments and financial performance.
Results
The results show that users' opinions have a significant impact on the financial performance of these organizations. Therefore, non-profit organizations should pay special attention to their users and donors to increase their satisfaction. Otherwise, the lack of proper management of the organization's actions can damage its credibility. Also, there is a need for further investigation to determine the cause of the negative relationship between the number of retweets and financial performance of organizations. Modeling of topics discussed by users shows that managers of non-profit organizations should focus more on building trust among their users, because the results of topic modeling show that support for the organization is the least frequent.
Keywords: Sentiment Analysis, Nonprofit Organizations, Topic Modeling, Social Network, Panel Data.
Management approaches in the field of smart
Mohammadakbar Sheikhzadeh; Mohammad Taghi Taghavifard; iman raeisivanani; Jahanyar Bamdadsoofi
Abstract
Crowdfunding is an effective way to achieve the non-profit goals of the charities which can be expanded using information and communication technology. In this regard, this study was conducted with the aim of providing an online donation intention model for collective financing of charitable institutions ...
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Crowdfunding is an effective way to achieve the non-profit goals of the charities which can be expanded using information and communication technology. In this regard, this study was conducted with the aim of providing an online donation intention model for collective financing of charitable institutions in Iran. The current research is an applied-developmental research in terms of its purpose, and it is considered a cross-sectional survey research from the data collection point of view. The statistical population in the qualitative section includes managers of charity institutions and university professors. Sampling was done using a purposeful method and theoretical saturation with 10 interviews. In the quantitative section, the views of 357 benefactors were used. The data collection tool is a semi-structured interview and a researcher-made questionnaire. To analyze the collected data, qualitative thematic analysis and partial least squares were used. The research findings showed that the technical infrastructure facilitating conditions, the quality and transparency of the website information, the security of the site and the application, and the preservation of privacy affect the perceived risk. Perceived risk affects the expectation of effort and the pleasure of helping others and leads to the expectation of performance and social influence. Finally, online donation intention is strengthened through trust.IntroductionToday, charity institutions in the country can expose to the various needs of the needy to the public using crowdfunding platforms, so that with the participation of people the necessary financial resources are provided and the problems of the needy and the deprived are solved. In this field, practical and scientific activities have also been carried out however one of the most important issues neglected from the researchers’ view point and activists in this field is the discussion of the desire or intention of online donation among benefactors. The best online donation platforms and the use of the latest technologies are not enough to attract public participation, and it is not easy to encourage and motivate people to donate online. This issue itself can be discussed from various aspects. On the one hand, the basic element of this method of financing is the maximum participation of people, and on the other hand, members of the society have little knowledge about the issue and do not have much desire to attend such calls.Therefore, in general, it can be said that the intention to donate online is a fundamental factor in the success of the efforts of the country's charities for crowdfunding. The basic question of this research is that: what is the online donation intention model for collective financing of charities in Iran?Literature ReviewCrowdfunding is an alternative way to finance a project with specific goals at a specific time through an online platform. The phenomenon of crowdfunding is emerging in the field of financing resources, goods and services in the modern digital field (Jiao and Yue, 2021). The current study focuses on the crowdfunding approach based on donations in Iranian charities. This method is mostly used in non-profit and philanthropic projects, and the participants do not expect any financial support, and are mostly used to finance charities, religious places, and civil institutions (Salido et al., 2021). In the crowdfunding method based on donation, the participants do not have any expectations for the provided financial support. The donor believes wholeheartedly in the correctness of his act and considers it to be socially beneficial. This model is usually used in financing civil institutions and charities (Van Thienbroek et al., 2023).MethodologyThis is an applied-developmental research. The population of participants in the qualitative section includes theoretical experts and experimental. Sampling was done with a purposeful method and theoretical saturation was achieved with 10 interviews. The sample size was estimated to be 357 people using Cochran's formula.The main tool for collecting research data is a semi-structured interview and a researcher-made questionnaire. And then, using ISM, the relationships between the main factors affecting the intention to donate online were determined by experts. The interview included 6 basic questions and was conducted in a semi-structured way. The research questionnaire includes 10 main constructs and 51 items with a five-point Likert scale. Thematic analysis was used to identify the categories of online donation intention for crowdfunding of charities in Iran. Data analysis was done in qualitative phase with MAXQDA software and in quantitative phase with Smart PLS software.ResultsThe results of the interviews were analyzed by the qualitative method of thematic analysis based on the six-step method of Etrid-Straling (2001). 830 open codes were identified in the first stage. After analysis and review, we reached 5 overarching themes, 10 organizing themes and 51 basic themes as below:Overarching themes: Social factors- Technical factors-Individual factors- Security factors- Consequence factors.Organizer themes: Performance expectation- Endeavour expectation- Social influence- Quality and transparency of website information- Technical infrastructure facilitating conditions- Trust- The joy of helping others- Perceived risk- Site and application security and privacy- Intention to donate online.DiscussionThe present study was conducted with the aim of presenting the online donation intention model for the collective financing of charitable organizations in Iran. Based on the results, it was determined that the facilitating conditions of the technical infrastructure, the quality and transparency of the website information, the security of the site and the application, and the protection of privacy affect the perceived risk. In the results of Zhang et al.'s (2023) and Shahidi and Kivani's (1401) studies, the technical infrastructure and website information components are also mentioned as important elements in the intention to donate online, and from this point of view, it is consistent with the results of the present study. It was also shown that the perceived risk affects the expectation of effort and the pleasure of helping others and leads to the expectation of performance and social influence. In the results of the studies of Hassanzadeh Sarostani et al. (2017) and Bass and Rushdi (2023), the importance of paying attention to the perceived risk is also mentioned, and from this point of view, it is consistent with the results of the present study. Finally, the results of the research showed that online donation intention is strengthened through trust. This importance has been confirmed in the results of Shahabi-Shojaei et al.'s study (1401).ConclusionThe present study was conducted with the aim of presenting the online donation intention model for the collective financing of charitable institutions in Iran. Based on the results, it was determined that the facilitating conditions of the technical infrastructure, the quality and transparency of the website information, the security of the site and the application, and the protection of privacy affect the perceived risk. It was also shown that the perceived risk affects the expectation of effort and the pleasure of helping others and leads to the expectation of performance and social influence. Finally, the results of the research showed that online donation intention is strengthened through trust.
Management approaches in the field of smart
vahid khashei varnamkhasti; Mahdi Ebrahimi; Shahram Khalil Nezhad; Fatemeh Motahari nejad
Abstract
.Today, digitization is considered as a requirement for all financial and monetary institutions and markets, and the banking industry is no exception. However, moving to digital banking is not an easy task for banks, and the digital revolution has become a major challenge for this industry. In order ...
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.Today, digitization is considered as a requirement for all financial and monetary institutions and markets, and the banking industry is no exception. However, moving to digital banking is not an easy task for banks, and the digital revolution has become a major challenge for this industry. In order to keep their business stable, banks need to launch digital platforms and create robust ecosystems around them that have the ability to evolve and adapt to the challenges caused by the chaotic and unpredictable environment in which organizations operate and the increase in internal inefficiencies. Therefore, this research was conducted with the purpose of modeling the generative mechanisms of the evolution of Iran's digital banking ecosystem. For this purpose, the qualitative content analysis method has been used. A semi-structured interview has been used to collect information using the opinions of experts in the field of digital banking. The analysis of the conducted interviews led to the identification of 674 themes, 181 codes, 59 subcategories, and 20 categories. The results of the research show that there are external factors, including cyber-attacks, political and governance obstacles, the increasing progress of communication and information technology, changes in the competitive environment, and conflicting investment relationships in the financial and banking fields, and internal factors, including the absence of strong security infrastructure in the network and weak security, weakness in digital human capital, changing needs and demands of actors, weakness in the traditional business model, and the lack of a management perspective regarding the growth of digital banking and regulatory and legal requirements, are the stimuli for the co-evolution of the elements of the digital banking ecosystem, which occurs with the activation of reinforcing and transformative generative mechanisms. The consequences of the evolution of the digital banking ecosystem can be classified into three levels: users, owner, and society.IntroductionIn the digital revolution, with the change in customer behavior and expectations and the behavior of businesses, the structure of the competition and the strategic context of the business world has changed, and the banking industry is not an exception to this rule; therefore, digitalization is considered a requirement for the banking industry. However, moving to digital banking is not an easy task for banks, and the digital revolution has become a major challenge for this industry. The previous studies have revealed that, so far, some banks have not been successful in facing the fundamental changes of the digital age and have not been able to take fundamental actions to stabilize their businesses. To this end, the first step to realizing these changes in the banking industry is a complete reconsideration of customer relations and the method of providing value to meet customer needs, business models, platforms, and ecosystems. Yet it is not enough to set up a digital platform ecosystem and just rapidly multiply it, as keeping it stable is also highly important as well. In fact, digital platform ecosystems that can be sustained over the long term are very rare. Therefore, digital platform ecosystems must have the ability to evolve and adapt to the challenges caused by the chaotic and unpredictable environment that organizations operate in as well as the increase of internal inefficiencies. Thus, besides the need for a complete rethinking of the digital banking ecosystem, the manner in which its evolutionary process has been in Iran's banking industry is important. Thus far, neither of these issues has been investigated in Iran which signifies the necessity of applied and fundamental research. The current research tries to fill this gap in the country.Research Question(s)What are the elements of the digital banking platform ecosystem and which will evolve in the future?What are the generative mechanisms of digital banking platform ecosystem evolution?What are the stimulating factors and consequences of the evolution of the digital banking platform ecosystem?Literature ReviewNowadays, digitization has become a strategic priority for the banking industry, and the establishment of digital banking requires creating a strong ecosystem around the digital banking platform and developing it. However, so far, there has not been any systematic and comprehensive approach regarding the evolution of the digital platform ecosystem. Previous studies identify digital platform ecosystems as highly evolving socio-technical arrangements that require rapid development and adaptation to ensure their long-term sustainability. Due to the lack of a clear conceptualization of the evolution of digital platform ecosystems, Stykova (2019) integrated different perspectives and proposed a novel conceptualization of the evolution of digital platform ecosystems. According to his studies, the evolution of the digital platform ecosystem delineates continuous changes in the digital platform ecosystem in relation to its actors, architecture, and governance. Through these changes, the simultaneous development of platform structures, infrastructure, functionalities, and governance regime occurs. The endogenous and exogenous events and factors emerging during the evolution of the digital platform ecosystem challenge the configuration of the ecosystem and lead to changing it. Staykova defines two types of generative mechanisms for the evolution of the digital platform ecosystem: transformative and reinforcing.MethodologyIn this research, the qualitative content analysis method is used. A group of experts in the field of digital banking in Iran's banking industry who have deep insight and the necessary knowledge about the subject of the research participated as the statistical population of this research. The purposive sampling method was employed for sampling and continued until the theoretical saturation was reached. Therefore, in this research, 21 interviews were conducted, and due to the incompleteness of some of them, in the end, by analyzing 18 interviews, the research questions were answered, and theoretical saturation was achieved.ResultsThe analysis of the interviews led to the identification of 674 themes, 181 codes, 59 subcategories, and 20 categories. Based on the results of categorization in thematic analysis and review of the theoretical foundations and the related literature, the elements of the digital banking platform ecosystem, including ecosystem actors, platform architecture, and platform governance were identified. Moreover, External factors, such as cyber-attacks, political and governance obstacles, the increasing progress of communication and information technology, changes in the competitive environment, and conflicting investment relationships in the financial and banking fields, as well as internal factors, such as the absence of strong security infrastructure in the network and weak security, weakness in digital human capital, changing needs and demands of actors, weakness in the traditional business model, and lack of management perspective regarding the growth of digital banking and regulatory and legal requirements, are the co-evolutionary stimuli of digital banking platform ecosystem elements, which appear as a result of the activation of generative, reinforcing and transformative mechanisms. Finally, the implications of the evolution of the ecosystem of the digital banking platform can be classified into three levels. The benefits of the evolution of the ecosystem for users are increasing the speed of providing and receiving services, gaining usefulness, improved security, freedom in providing and receiving services, ease of access, and co-creation of value. The implications of evolution for the owner (organization) are the Orchestrator role of the owner, dynamic capability, market size, increased revenue, increased productivity, and business sustainability. Furthermore, the outcomes of the evolution of the ecosystem at the community level include social, environmental, and economic benefits, which are indicators of reaching the maturity stage in the life cycle of the digital banking platform ecosystem.
Management approaches in the field of smart
vahideh alipoor; Mohammadreza Saadi; atefeh mehri bazghaleh
Abstract
This study aimed to investigate the effect of brand interaction through social media on brand loyalty through brand equity. The research is a descriptive research of the correlational type in terms of practical purpose and based on the method. The statistical population of this research includes the ...
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This study aimed to investigate the effect of brand interaction through social media on brand loyalty through brand equity. The research is a descriptive research of the correlational type in terms of practical purpose and based on the method. The statistical population of this research includes the customers of three Iranian brands, My, Cinere and Callista. The size of the population is considered unlimited and the G-POWER tool was used to determine the sample size and the statistical sample size was calculated to be 199 people. A simple random sampling method was chosen for data collection and the data collection required in this research was done using a localized standard questionnaire. In order to confirm content validity, CVI and CVR and expert panel were used. In order to determine the reliability of the questionnaire, Cronbach's alpha coefficient, combined reliability and homogeneous reliability were used. Because the data do not follow normal modeling; Therefore, structural equation model and partial least squares method and PLS software were used for hypothesis testing and path analysis. The results of this study showed that customer interaction with the brand through social media can have a positive effect on brand loyalty. Also, brand equity was a positive mediating factor in the relationship between brand interaction and brand loyalty. Among the three cognitive, emotional and behavioral paths, the behavioral path with a path coefficient of 0.630 is the best path in influencing customer interaction with the brand on brand loyalty.
