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Saeed Rouhi Shalemaie, Mohammad Khandan, Ali Shabani,
Volume 11, Issue 2 (9-2024)
Volume 11, Issue 2 (9-2024)
Abstract
Introduction
The present research aims to design a model for intergenerational knowledge sharing in order to identify the dimensions and rank the Factors and Components influencing intergenerational knowledge sharing in the car leasing industry.
Methods and Materoal
Considering the conceptual framework of the present study and the nature and type of available data and information for presenting a conceptual model of intergenerational knowledge sharing in the leasing industry, the research method utilized is an exploratory mixed-methods approach. This study is fundamental in its outcomes, has a practical nature, and is also critical in terms of its paradigm. The statistical population of this research comprises two sections: the qualitative part consists of 17 experts and specialists from the leasing industry, while the quantitative part includes a total of 970 employees currently working in this industry. Based on Cochran's formula and with a 95% margin of error, a sample of 275 individuals was selected. To ensure greater confidence, an additional 25% was added to the minimum sample size, leading to 343 questionnaires being sent to employees. Ultimately, 336 complete and valid questionnaires were returned, which were used for analysis in this research. Non-probability purposive sampling was employed for sample selection. Purposive sampling involves selecting a portion of the population based on the researcher's (or experts and specialists') judgment. In this method, sample acceptance criteria are defined, and individuals are selected for the survey regarding the research subject based on these criteria. In this research, the criteria for purposive sampling to select experts in the qualitative section were: 1) Leasing industry experts with more than 5 years of experience. 2) Leasing industry experts holding master's and doctoral degrees. After conducting interviews with selected individuals and upon reaching saturation in responses, with the agreement of the supervisors and advisors, the theoretical saturation was achieved, and the number of samples is detailed in the table below. Additionally, in the quantitative section, Cochran's formula was utilized, resulting in a selection of 336 employees from the leasing industry through simple random sampling. The data collection for this research was based on library studies including books, articles, websites, and relevant Persian and English internet information portals. Given the scarcity of library resources on the research topic, the most significant source used has been the internet and various databases, which has added to the importance of the research and the currency of information. For data collection in both qualitative and quantitative sections, field methods and tools such as semi-structured interviews and questionnaires were employed, which will be elaborated upon further. Semi-structured interviews are among the most common types of interviews used in social qualitative research. These interviews can be both structured and unstructured, and are sometimes referred to as in-depth interviews, where all respondents are asked similar questions and can freely answer the questions. In this research, for the semi-structured interviews, common questions were utilized based on the opinions of experts and professionals in the leasing industry, and the responses derived from these questions were transformed into specific components through descriptive analysis with the help of open, axial, and selective coding. For conducting field studies, a questionnaire has been utilized. Accordingly, based on the research objectives and questions, the research tool, namely the questionnaire, was designed. To gather information, both the questionnaire and semi-structured interviews were employed. In this research, categories were used to analyze the semi-structured interviews. The categories are often labeled as codes or keywords; however, anything that is labeled has the capability to organize and systematize the data, often functioning even as analytical codes. Analytical codes are the result of an analytical process that goes beyond merely identifying a topic. The coding of information was also analyzed using MaxQDA software. After collecting the conducted interviews and extracting their indicators, we entered them into MaxQDA and categorized them into groups and sets, each related to one of the main indicators. In the code system section of MAXQDA software, we established a hierarchical arrangement of codes and subcodes. In this research, descriptive statistics including frequency, percentage, mean, and standard deviation were used to analyze the obtained data from the samples. Additionally, in the inferential statistics section, the structural equation modeling method was employed. These analyses were conducted using SPSS and Smart PLS 2.0 statistical software.
Resultss and Discussion
The findings in the quantitative section indicated that 55 percent of the respondents were male and 45 percent were female. The majority of the sample had over 15 years of work experience (80 percent). The education level of 80 percent of the individuals was at the master's level, and the most common age range in the group was 30 to 50 years, accounting for 90 percent. The qualitative findings showed that 43.8 percent of the respondents were male and 56.3 percent were female. The majority of the sample had over 15 years of work experience (51.2 percent). The education level of 45.5 percent of individuals was at the master's or doctoral level, and the most common age range in this group was 40 to 50 years, comprising 39.6 percent. The results indicated that the standard deviation values were mostly below 1, with only a few below 2. This finding suggests that the data has low dispersion, and responses were primarily in alignment with each other. Additionally, to assess the normality or non-normality of the distribution of variables among the respondents, skewness and kurtosis values were utilized. Given that the skewness and kurtosis values were below 2, we can conclude that the data has a normal distribution. The findings indicated that the mean of the knowledge sharing variable is above the expected level, with a mean of 3.85 for knowledge sharing. Thus, the evaluation of the sample's opinions showed that the mean of the items related to the knowledge sharing variable is above average. Descriptive statistics revealed that the mean of the external environment variable is also above the expected level, with an average of 3.77. Consequently, the evaluation of the sample's opinions indicated that the mean of the items related to the external environment variable is above average as well. A review of the descriptive statistics showed that the mean of the innovation variable is higher than the expected level. Innovation had an average score of 3.65. Therefore, the evaluation of the sample's opinions indicated that the mean scores related to the variable of innovation are above the average level. The results obtained from the descriptive statistics review showed that the mean of the foresight variable is higher than the expected level, with an average of 3.43. Consequently, the evaluation of the sample's opinions indicated that the mean scores related to the variable of foresight are above the average level. The results from the descriptive statistics review indicated that the mean of the reactive variable is higher than the expected level, with an average of 3.88. Therefore, the evaluation of the sample's opinions showed that the mean scores related to the reactive variable are above the average level. The results obtained from the descriptive statistics review indicated that the mean of the analytical variable is higher than the expected level, with an average of 3.79. Hence, the evaluation of the sample's opinions indicated that the mean scores related to the analytical variable are above the average level. The results from the descriptive statistics review showed that the mean of the information technology governance variable is higher than the expected level, with an average of 3.71. Therefore, the evaluation of the sample's opinions indicated that the mean scores related to the information technology governance variable are above the average level. The results from the descriptive statistics review showed that the mean of the organizational structural variable is higher than the expected level, with an average of 3.57. Thus, the evaluation of the sample's opinions indicated that the mean scores related to the organizational structural variable are above the average level. The results obtained from the descriptive statistics review indicated that the mean of the learning organization variable is higher than the expected level, with an average of 3.71. Thus, the evaluation of the sample's opinions indicated that the mean scores related to the learning organization variable are above the average level. The results from the descriptive statistics review showed that the mean of the organizational learning variable is higher than the expected level, with an average of 3.54. The evaluation of the sample opinions indicated that the mean of the items related to the variable of organizational learning is above the average level. The results from the descriptive statistics showed that the mean for the variable of knowledge management is above the expected level, with knowledge management having a mean of 3.50. Therefore, the assessment of the sample opinions revealed that the mean of the items related to the variable of knowledge management is above the average level. The components of knowledge sharing, external environment, innovation, foresight, responsiveness, analysis, information technology governance, organizational structure, learning organization, organizational learning, and knowledge management have a direct and significant impact on inter-generational knowledge sharing in the leasing industry. Based on the results from the structural equation modeling, it is observed that knowledge sharing has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.362. Hence, it can be said that for a 36% increase in knowledge sharing, the transfer of inter-generational knowledge sharing also increases by 36%. The external environment has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.331. Therefore, it can be stated that for a 33% increase in the external environment, the transfer of inter-generational knowledge sharing also increases by 33%. Innovation has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.322. Consequently, it can be said that for a 32% increase in the innovation environment, the transfer of inter-generational knowledge sharing also increases by 32%. Foresight has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.376. Thus, it can be stated that for a 38% increase in foresight, the transfer of inter-generational knowledge sharing also increases by 38%. Responsiveness has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.301. Therefore, it can be concluded that for a 30% increase in responsiveness, the transfer of inter-generational knowledge sharing also increases by 30%. An analysis of intergenerational knowledge sharing shows a significant and positive relationship, with a standardized effect size of 0.338. Therefore, it can be said that for every 34% increase in the analytic aspect, intergenerational knowledge sharing also increases by 34%. The governance of information technology has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.329. Thus, it can be stated that for every 33% increase in information technology governance, intergenerational knowledge sharing also increases by 33%. Organizational structure has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.377. Accordingly, it can be inferred that for every 38% increase in organizational structure, intergenerational knowledge sharing increases by 38%. A learning organization has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.347. Thus, it can be said that for every 35% increase in learning organizations, intergenerational knowledge sharing also increases by 35%. Organizational learning has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.353. Therefore, it can be stated that for every 35% increase in organizational learning, intergenerational knowledge sharing increases by 35%. Knowledge management shows a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.967. Thus, it can be concluded that for every 97% increase in knowledge management, intergenerational knowledge sharing also increases by 97%.
Conclusion
Based on the results obtained, the components (knowledge sharing, external environment, innovation, foresight, reaction, analytical, information technology governance, organizational structure, learning organization, organizational learning, knowledge management) were identified as the main components, while the components (planning and organizing information technology, acquiring and implementing information technology, delivery and support for information technology, monitoring and evaluating information technology, complexity, formalization, centralization and decentralization, personal capabilities and skills, patterns and mental models, shared vision and goals, team learning, systems thinking) were considered as sub-components affecting intergenerational knowledge sharing in the leasing industry. According to the assessments conducted, the components (knowledge management (97%), organizational structure (38%), foresight (38%), knowledge sharing (36%), organizational learning (35%), learning organization (35%), analytical (34%), external environment (33%), information technology governance (33%), innovation (32%), and reaction (30%)) ranked in this order as having the highest impact on intergenerational knowledge sharing in the leasing industry. It was found that, from the specialists' perspective, the intergenerational knowledge sharing model in the leasing industry aligns well with the needs of this industry. This knowledge sharing model can enhance operational processes, improve service quality, and increase productivity. Furthermore, this model can facilitate the transfer of experiences and knowledge to future generations, thereby contributing to the advancement of the leasing industry. Overall, specialists believed that the intergenerational knowledge sharing model in the leasing industry is well-suited to its needs and can support its performance and progress. Based on the analysis obtained and the identification of components (knowledge sharing, external environment, innovation, foresight, reaction, analytical, information technology governance, organizational structure, learning organization, organizational learning, knowledge management), it can be concluded that all these components present a suitable model for improving the performance of the automotive leasing industry, and it is recommended that this model be considered for advancing the goals and success of this industry.
The present research aims to design a model for intergenerational knowledge sharing in order to identify the dimensions and rank the Factors and Components influencing intergenerational knowledge sharing in the car leasing industry.
Methods and Materoal
Considering the conceptual framework of the present study and the nature and type of available data and information for presenting a conceptual model of intergenerational knowledge sharing in the leasing industry, the research method utilized is an exploratory mixed-methods approach. This study is fundamental in its outcomes, has a practical nature, and is also critical in terms of its paradigm. The statistical population of this research comprises two sections: the qualitative part consists of 17 experts and specialists from the leasing industry, while the quantitative part includes a total of 970 employees currently working in this industry. Based on Cochran's formula and with a 95% margin of error, a sample of 275 individuals was selected. To ensure greater confidence, an additional 25% was added to the minimum sample size, leading to 343 questionnaires being sent to employees. Ultimately, 336 complete and valid questionnaires were returned, which were used for analysis in this research. Non-probability purposive sampling was employed for sample selection. Purposive sampling involves selecting a portion of the population based on the researcher's (or experts and specialists') judgment. In this method, sample acceptance criteria are defined, and individuals are selected for the survey regarding the research subject based on these criteria. In this research, the criteria for purposive sampling to select experts in the qualitative section were: 1) Leasing industry experts with more than 5 years of experience. 2) Leasing industry experts holding master's and doctoral degrees. After conducting interviews with selected individuals and upon reaching saturation in responses, with the agreement of the supervisors and advisors, the theoretical saturation was achieved, and the number of samples is detailed in the table below. Additionally, in the quantitative section, Cochran's formula was utilized, resulting in a selection of 336 employees from the leasing industry through simple random sampling. The data collection for this research was based on library studies including books, articles, websites, and relevant Persian and English internet information portals. Given the scarcity of library resources on the research topic, the most significant source used has been the internet and various databases, which has added to the importance of the research and the currency of information. For data collection in both qualitative and quantitative sections, field methods and tools such as semi-structured interviews and questionnaires were employed, which will be elaborated upon further. Semi-structured interviews are among the most common types of interviews used in social qualitative research. These interviews can be both structured and unstructured, and are sometimes referred to as in-depth interviews, where all respondents are asked similar questions and can freely answer the questions. In this research, for the semi-structured interviews, common questions were utilized based on the opinions of experts and professionals in the leasing industry, and the responses derived from these questions were transformed into specific components through descriptive analysis with the help of open, axial, and selective coding. For conducting field studies, a questionnaire has been utilized. Accordingly, based on the research objectives and questions, the research tool, namely the questionnaire, was designed. To gather information, both the questionnaire and semi-structured interviews were employed. In this research, categories were used to analyze the semi-structured interviews. The categories are often labeled as codes or keywords; however, anything that is labeled has the capability to organize and systematize the data, often functioning even as analytical codes. Analytical codes are the result of an analytical process that goes beyond merely identifying a topic. The coding of information was also analyzed using MaxQDA software. After collecting the conducted interviews and extracting their indicators, we entered them into MaxQDA and categorized them into groups and sets, each related to one of the main indicators. In the code system section of MAXQDA software, we established a hierarchical arrangement of codes and subcodes. In this research, descriptive statistics including frequency, percentage, mean, and standard deviation were used to analyze the obtained data from the samples. Additionally, in the inferential statistics section, the structural equation modeling method was employed. These analyses were conducted using SPSS and Smart PLS 2.0 statistical software.
Resultss and Discussion
The findings in the quantitative section indicated that 55 percent of the respondents were male and 45 percent were female. The majority of the sample had over 15 years of work experience (80 percent). The education level of 80 percent of the individuals was at the master's level, and the most common age range in the group was 30 to 50 years, accounting for 90 percent. The qualitative findings showed that 43.8 percent of the respondents were male and 56.3 percent were female. The majority of the sample had over 15 years of work experience (51.2 percent). The education level of 45.5 percent of individuals was at the master's or doctoral level, and the most common age range in this group was 40 to 50 years, comprising 39.6 percent. The results indicated that the standard deviation values were mostly below 1, with only a few below 2. This finding suggests that the data has low dispersion, and responses were primarily in alignment with each other. Additionally, to assess the normality or non-normality of the distribution of variables among the respondents, skewness and kurtosis values were utilized. Given that the skewness and kurtosis values were below 2, we can conclude that the data has a normal distribution. The findings indicated that the mean of the knowledge sharing variable is above the expected level, with a mean of 3.85 for knowledge sharing. Thus, the evaluation of the sample's opinions showed that the mean of the items related to the knowledge sharing variable is above average. Descriptive statistics revealed that the mean of the external environment variable is also above the expected level, with an average of 3.77. Consequently, the evaluation of the sample's opinions indicated that the mean of the items related to the external environment variable is above average as well. A review of the descriptive statistics showed that the mean of the innovation variable is higher than the expected level. Innovation had an average score of 3.65. Therefore, the evaluation of the sample's opinions indicated that the mean scores related to the variable of innovation are above the average level. The results obtained from the descriptive statistics review showed that the mean of the foresight variable is higher than the expected level, with an average of 3.43. Consequently, the evaluation of the sample's opinions indicated that the mean scores related to the variable of foresight are above the average level. The results from the descriptive statistics review indicated that the mean of the reactive variable is higher than the expected level, with an average of 3.88. Therefore, the evaluation of the sample's opinions showed that the mean scores related to the reactive variable are above the average level. The results obtained from the descriptive statistics review indicated that the mean of the analytical variable is higher than the expected level, with an average of 3.79. Hence, the evaluation of the sample's opinions indicated that the mean scores related to the analytical variable are above the average level. The results from the descriptive statistics review showed that the mean of the information technology governance variable is higher than the expected level, with an average of 3.71. Therefore, the evaluation of the sample's opinions indicated that the mean scores related to the information technology governance variable are above the average level. The results from the descriptive statistics review showed that the mean of the organizational structural variable is higher than the expected level, with an average of 3.57. Thus, the evaluation of the sample's opinions indicated that the mean scores related to the organizational structural variable are above the average level. The results obtained from the descriptive statistics review indicated that the mean of the learning organization variable is higher than the expected level, with an average of 3.71. Thus, the evaluation of the sample's opinions indicated that the mean scores related to the learning organization variable are above the average level. The results from the descriptive statistics review showed that the mean of the organizational learning variable is higher than the expected level, with an average of 3.54. The evaluation of the sample opinions indicated that the mean of the items related to the variable of organizational learning is above the average level. The results from the descriptive statistics showed that the mean for the variable of knowledge management is above the expected level, with knowledge management having a mean of 3.50. Therefore, the assessment of the sample opinions revealed that the mean of the items related to the variable of knowledge management is above the average level. The components of knowledge sharing, external environment, innovation, foresight, responsiveness, analysis, information technology governance, organizational structure, learning organization, organizational learning, and knowledge management have a direct and significant impact on inter-generational knowledge sharing in the leasing industry. Based on the results from the structural equation modeling, it is observed that knowledge sharing has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.362. Hence, it can be said that for a 36% increase in knowledge sharing, the transfer of inter-generational knowledge sharing also increases by 36%. The external environment has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.331. Therefore, it can be stated that for a 33% increase in the external environment, the transfer of inter-generational knowledge sharing also increases by 33%. Innovation has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.322. Consequently, it can be said that for a 32% increase in the innovation environment, the transfer of inter-generational knowledge sharing also increases by 32%. Foresight has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.376. Thus, it can be stated that for a 38% increase in foresight, the transfer of inter-generational knowledge sharing also increases by 38%. Responsiveness has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.301. Therefore, it can be concluded that for a 30% increase in responsiveness, the transfer of inter-generational knowledge sharing also increases by 30%. An analysis of intergenerational knowledge sharing shows a significant and positive relationship, with a standardized effect size of 0.338. Therefore, it can be said that for every 34% increase in the analytic aspect, intergenerational knowledge sharing also increases by 34%. The governance of information technology has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.329. Thus, it can be stated that for every 33% increase in information technology governance, intergenerational knowledge sharing also increases by 33%. Organizational structure has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.377. Accordingly, it can be inferred that for every 38% increase in organizational structure, intergenerational knowledge sharing increases by 38%. A learning organization has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.347. Thus, it can be said that for every 35% increase in learning organizations, intergenerational knowledge sharing also increases by 35%. Organizational learning has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.353. Therefore, it can be stated that for every 35% increase in organizational learning, intergenerational knowledge sharing increases by 35%. Knowledge management shows a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.967. Thus, it can be concluded that for every 97% increase in knowledge management, intergenerational knowledge sharing also increases by 97%.
Conclusion
Based on the results obtained, the components (knowledge sharing, external environment, innovation, foresight, reaction, analytical, information technology governance, organizational structure, learning organization, organizational learning, knowledge management) were identified as the main components, while the components (planning and organizing information technology, acquiring and implementing information technology, delivery and support for information technology, monitoring and evaluating information technology, complexity, formalization, centralization and decentralization, personal capabilities and skills, patterns and mental models, shared vision and goals, team learning, systems thinking) were considered as sub-components affecting intergenerational knowledge sharing in the leasing industry. According to the assessments conducted, the components (knowledge management (97%), organizational structure (38%), foresight (38%), knowledge sharing (36%), organizational learning (35%), learning organization (35%), analytical (34%), external environment (33%), information technology governance (33%), innovation (32%), and reaction (30%)) ranked in this order as having the highest impact on intergenerational knowledge sharing in the leasing industry. It was found that, from the specialists' perspective, the intergenerational knowledge sharing model in the leasing industry aligns well with the needs of this industry. This knowledge sharing model can enhance operational processes, improve service quality, and increase productivity. Furthermore, this model can facilitate the transfer of experiences and knowledge to future generations, thereby contributing to the advancement of the leasing industry. Overall, specialists believed that the intergenerational knowledge sharing model in the leasing industry is well-suited to its needs and can support its performance and progress. Based on the analysis obtained and the identification of components (knowledge sharing, external environment, innovation, foresight, reaction, analytical, information technology governance, organizational structure, learning organization, organizational learning, knowledge management), it can be concluded that all these components present a suitable model for improving the performance of the automotive leasing industry, and it is recommended that this model be considered for advancing the goals and success of this industry.
Yaghoub Norouzi, Sima Tarashi, Narehreh Jafarifar,
Volume 11, Issue 2 (9-2024)
Volume 11, Issue 2 (9-2024)
Abstract
A museum website is an online platform created by a museum to provide information about its collections, exhibitions, programs, and services. The website serves as an important tool for engagement, allowing both local visitors and distant audiences to connect with the museum’s officials. The Persian web pages of Iranian museums play a crucial role. Persian web pages help catalog and document Iranian artifacts, ensuring that cultural heritage is preserved for future generations. They serve as educational platforms, providing information about the cultural significance and history of the items in their collections. By showcasing exhibits and events, these websites can attract both domestic and international tourists interested in Iranian culture and history. They facilitate cultural exchange by providing insights into Iranian traditions, encouraging global visitors to explore Iran's cultural treasures. Persian web pages allow for better outreach to the Persian-speaking population, ensuring that the museum information is easily accessible to locals. They enable museums to engage with the public through online exhibits, virtual tours, and interactive content, making them more accessible to those unable to visit in person. Academics and students can use these web pages as valuable resources for research on Persian art, history, and archaeology. They often provide access to digital archives, scholarly articles, and other resources to support education and research efforts. Museums can announce community events, workshops, and educational programs, fostering a sense of community involvement. By highlighting local culture and history, these web pages help reinforce a sense of national identity and pride among Iranians. Persian web pages support the use of the Persian language, promoting literacy and engagement among Persian speakers. They provide information that is culturally relevant and linguistically accessible for Iranian citizens, particularly in regions where other languages may dominate. Therefore, Persian web pages of Iranian museums are vital for promoting cultural heritage, enhancing tourism, facilitating research, engaging communities, and ensuring that the rich history of Iran is preserved and shared with the world. Evaluating the user interface of Persian web pages for Iranian museums is essential for several reasons:
- Cultural Significance: Museums are custodians of culture and heritage. A well-designed interface can effectively represent and communicate cultural values and historical narratives.
- User Experience: A user-friendly interface enhances the visitor experience, making it easier for users to access information about exhibits, events, and educational resources.
- Accessibility: Evaluating the interface helps ensure that it is accessible to a diverse audience, including those with disabilities, ensuring that everyone can engage with the museum’s offerings.
- Information Dissemination: Museums play a crucial role in education. An effective interface helps disseminate information about collections, research, and educational programs efficiently.
- Tourism Promotion:An attractive and functional website can promote tourism, attracting both domestic and international visitors to Iranian museums and cultural sites.
- Technological Advancements: With the rapid development of web technologies, it’s important to regularly evaluate and update interfaces to meet current standards and user expectations.
- Feedback Mechanism: Evaluation can provide insights into user preferences and behaviors, allowing museums to refine their digital strategies and improve overall engagement.
In conclusion, the evaluation of user interfaces is crucial for enhancing the effectiveness and appeal of museum web pages and ensuring that they serve their educational and cultural missions. The present study was carried out with the aim of evaluating the user interface of the Persian web pages of Iranian museums and comparing them with the criteria found in the texts and sources. Evaluating the user interface of museum websites can provide valuable insights for both designers and users. Present Findings Illustrate the strengths and weaknesses of the current websites. Provides practical suggestions for improvement. This approach not only highlights the current status but also aims at enhancing the user experience in the future.
Methods and Materoal
The research is of an applied type and it was carried out by a survey method of the type of Heuristic Evaluation. The statistical population of the research includes 10 museum websites under the supervision of the Ministry of Cultural Heritage of Iran which include:
Methods and Materoal
The research is of an applied type and it was carried out by a survey method of the type of Heuristic Evaluation. The statistical population of the research includes 10 museum websites under the supervision of the Ministry of Cultural Heritage of Iran which include:
- Iran Communication Museum: https://www.ict.gov.ir/tour/IranCommunicationMuseum/
- Razavi Museum: https://museum.razavi.ir/
- Malek Museum: http://malekmuseum.org/
- Iran national Museum: https://irannationalmuseum.ir/fa/
- Sa'dabad Museum Complex: http://sadmu.ir/
- Niavaran Museum: http://www.niavaranmu.ir/
- Golestan Museum: http://www.golestanpalace.ir/
- Tehran Museum of Contemporary Art: https://galleryinfo.ir/
- National Museum of the Islamic Revolution and Holy Defense: https://iranrhdm.ir/
- Iranian National Museum of Medical Sciences History: https://museum.tums.ac.ir/
The research tool is the evaluation list based on the criteria found in the texts and sources including 10 main indicators (search, Cohesion, guidance, Show information, Page design, navigation, User interface language, Simplicity, User control, Error correction) and 103 sub-components. Here’s an elaboration on each of the 10 indicators specifically regarding the user interface of Iranian museum websites:
- Searching
- Accessibility: The search bar should be prominently placed, often at the top of the page, to ensure users can easily find it.
- Relevancy: The search results should be accurately related to user queries, featuring filtering options for better refinement (e.g., categories like exhibits, events, or collections).
- Consistency
- Consistent Design: Elements such as fonts, colors, and icons should remain the same across all pages, facilitating an intuitive experience.
- Unified Message: The website should consistently reflect the museum's themes (e.g., art, history) in both content and visual design.
3. Guidance
- Supportive Resources: There should be a dedicated section for user assistance, possibly including FAQs and tips on how to navigate the site.
- Clear Pathways: Guides or prompts should be available to help users navigate complex information or sections, enhancing overall orientation.
4. Presentation
- Effective Communication: Information should be presented in a straightforward manner, avoiding overly complex language.
- Engaging Visuals: The use of high-quality images and multimedia should enhance content comprehension and engagement, making exhibits come alive.
5. Design
- Visual Appeal: The design should authentically reflect the museum’s identity and cultural significance, employing a harmonious color palette and suitable typography.
- Logical Layout: Content should be arranged in a way that follows a natural reading order, ensuring that users can scan and find information quickly.
6. Navigation
- Intuitive Paths: Users should navigate seamlessly through sections, with clear labels for each category.
- Utilization of Breadcrumbs: Breadcrumb navigation helps users understand their current position on the website and easily backtrack if needed.
7. Language
- Cultural Relevance: The language used should resonate with both local users and international visitors, with translations where necessary.
- Clarity and Simplicity: Technical terms should be minimized or clarified to ensure accessibility for all users, including those who may not be experts.
8. Simplicity
- Uncluttered Design: The interface should prioritize essential information and minimize distractions, leading to easier navigation.
- Focus on Key Functions: Critical features like ticket booking or exhibit details should be straightforward and easy to access.
9. User Control
- Customization Options: Users should be able to adjust settings (like text size or language) to fit their preferences.
- Easy Navigation: The site should allow for quick changes between sections without losing previously entered data or context.
10. Error management (recovery)
- User-Friendly Feedback: When an error occurs (like a broken link), users should receive a clear message explaining the issue and offering solutions.
- Recovery Options: Users should have straightforward options to undo actions, such as going back to previous pages or reattempting forms without re-entering all data.
By focusing on these indicators, Iranian museum websites can enhance their usability and create a more engaging experience for visitors, helping to promote cultural heritage effectively.
For each index, a score between 1 and 3 was considered according to the degree of importance, and in this way, the criteria compiled in the list were ranked with 3 degrees of importance. The points obtained by each of the studied museum sites in relation to each of the components were multiplied by the average coefficients obtained (weighted average of the criteria) by the components from the Delphi panel. It should be noted that in this research, the final rank of the following indicators was obtained based on the Delphi panel presented in the doctoral dissertation of Hariri & Norouzi (2011). Data collection was done using the direct observation method, in this way, each of the components of the user interface design evaluation list was examined on the website page under study and the points obtained from it were recorded. Scores were given based on yes, present (1) and no, not present (0). Also, due to the quality of some of the sub-components, it was possible that the studied site did not comply with them equally, or in other words, absolute presence or absence could not be applied to them. Regarding these components, in addition to two levels, i.e. zero and one, 50% of the average score was also used. Wilcoxon signed-rank test and Friedman test were used to analyze the data. Excell, SPSS, Oegin pro Origin lab software were used.
Resultss and Discussion
Indicator: Simplicity with average compliance 100 % , Indicator: Error management (recovery) with average compliance 98.13 %, Indicator: language with average compliance 97.51 %, Indicator: design with average compliance 73.92 %, Indicator: consistency with average compliance 63.03 %, Indicator: Guidance with average compliance 61.22 %,Indicator: presentation with average compliance 50.36 %, Indicator: navigation with average compliance 48.89 %, Indicator: Searching with average compliance 26.83 % and Indicator: User control with average compliance 21.90 %, has been observed by ten museum websites under study respectively. Therefore, The findings showed that among the 10 main indicators, the criteria of simplicity, Error management (recovery) and language scored 100%, 98.13% and 97.51%, respectively. The criteria of User control and Searching had the lowest compliance with the components of the evaluation list with 21.9% and 26.83%, respectively.Among the statistical population. Niavaran Museum site had the highest level of quality compliance with 71.71% . National Museum of the Islamic Revolution and Holy Defense with 68.55%, Iran national Museum with 68.03%, Iranian National Museum of Medical Sciences History with 67.96%, Razavi Museum with 67.7%, Malek Museum with 66.73%, Sa'dabad Museum Complex with 66.73%, Golestan Museum with 66.59%, Tehran Museum of Contemporary Art with 55.27%, Iran Communication Museum with 42.52 were placed in the next positions.Iran Communication Museum had the lowest level of quality compliance with the evaluation list with 42.52%.
Conclusion
Friedman test is one of the famous non-parametric tests that was used to determine the order of importance of the factors mentioned in the research and to rank the sites. In Friedman test, since the answers are interdependent, comparison can be made in terms of rank. For this reason, Friedman test was used to rank the websites of the studied museums in compliance with the criteria. The results of the investigation of the Friedman test showed that Niavaran Museum Website with a graded average 6.65, National Museum of the Islamic Revolution and Holy Defense Website with a graded average 6.5, Iranian National Museum of Medical Sciences History Website with a graded average 6.45, Razavi Museum Website with a graded average 6.35, Iran national Museum Website with a graded average 6.1, Malek Museum Website with a graded average 5.8, Sa'dabad Museum Complex Website with a graded average 5.8, Golestan Museum Website with a graded average 5.75, Tehran Museum of Contemporary Art Website with a graded average 4 and Iran Communication Museum Website with a graded average 1.6 won the first to 10th rank respectively. Results Wilcoxon signed-rank test showed Among the ten indicators of the user interface evaluation list in the studied museum websites, Simplicity Criterion fully complied; Criteria: Guidance, presentation, navigation on average, more than 50% have been observed; criterias: Searching, Consistency, Design, Language, User control, Error management (recovery)on average, less than 50% have been observed. The results of the investigation of the research hypothesis showed that the studied museum sites differ in respect of user interface evaluation indicators, and the websites of the studied museums have acted differently in terms of compliance with the indicators. Therefore, it was concluded that the user interface designers of each of the sites did not have similar approaches. In order to achieve unity and success as much as possible, it is suggested that a working group be formed to share knowledge and skills among the relevant officials of the studied museums, so as to improve the existing situation. The evaluation list presented in the current research can be adapted for other Iranian museum websites and can be considered by the stakeholders as a proposed model of the user interface.
- Unified Message: The website should consistently reflect the museum's themes (e.g., art, history) in both content and visual design.
3. Guidance
- Supportive Resources: There should be a dedicated section for user assistance, possibly including FAQs and tips on how to navigate the site.
- Clear Pathways: Guides or prompts should be available to help users navigate complex information or sections, enhancing overall orientation.
4. Presentation
- Effective Communication: Information should be presented in a straightforward manner, avoiding overly complex language.
- Engaging Visuals: The use of high-quality images and multimedia should enhance content comprehension and engagement, making exhibits come alive.
5. Design
- Visual Appeal: The design should authentically reflect the museum’s identity and cultural significance, employing a harmonious color palette and suitable typography.
- Logical Layout: Content should be arranged in a way that follows a natural reading order, ensuring that users can scan and find information quickly.
6. Navigation
- Intuitive Paths: Users should navigate seamlessly through sections, with clear labels for each category.
- Utilization of Breadcrumbs: Breadcrumb navigation helps users understand their current position on the website and easily backtrack if needed.
7. Language
- Cultural Relevance: The language used should resonate with both local users and international visitors, with translations where necessary.
- Clarity and Simplicity: Technical terms should be minimized or clarified to ensure accessibility for all users, including those who may not be experts.
8. Simplicity
- Uncluttered Design: The interface should prioritize essential information and minimize distractions, leading to easier navigation.
- Focus on Key Functions: Critical features like ticket booking or exhibit details should be straightforward and easy to access.
9. User Control
- Customization Options: Users should be able to adjust settings (like text size or language) to fit their preferences.
- Easy Navigation: The site should allow for quick changes between sections without losing previously entered data or context.
10. Error management (recovery)
- User-Friendly Feedback: When an error occurs (like a broken link), users should receive a clear message explaining the issue and offering solutions.
- Recovery Options: Users should have straightforward options to undo actions, such as going back to previous pages or reattempting forms without re-entering all data.
By focusing on these indicators, Iranian museum websites can enhance their usability and create a more engaging experience for visitors, helping to promote cultural heritage effectively.
For each index, a score between 1 and 3 was considered according to the degree of importance, and in this way, the criteria compiled in the list were ranked with 3 degrees of importance. The points obtained by each of the studied museum sites in relation to each of the components were multiplied by the average coefficients obtained (weighted average of the criteria) by the components from the Delphi panel. It should be noted that in this research, the final rank of the following indicators was obtained based on the Delphi panel presented in the doctoral dissertation of Hariri & Norouzi (2011). Data collection was done using the direct observation method, in this way, each of the components of the user interface design evaluation list was examined on the website page under study and the points obtained from it were recorded. Scores were given based on yes, present (1) and no, not present (0). Also, due to the quality of some of the sub-components, it was possible that the studied site did not comply with them equally, or in other words, absolute presence or absence could not be applied to them. Regarding these components, in addition to two levels, i.e. zero and one, 50% of the average score was also used. Wilcoxon signed-rank test and Friedman test were used to analyze the data. Excell, SPSS, Oegin pro Origin lab software were used.
Resultss and Discussion
Indicator: Simplicity with average compliance 100 % , Indicator: Error management (recovery) with average compliance 98.13 %, Indicator: language with average compliance 97.51 %, Indicator: design with average compliance 73.92 %, Indicator: consistency with average compliance 63.03 %, Indicator: Guidance with average compliance 61.22 %,Indicator: presentation with average compliance 50.36 %, Indicator: navigation with average compliance 48.89 %, Indicator: Searching with average compliance 26.83 % and Indicator: User control with average compliance 21.90 %, has been observed by ten museum websites under study respectively. Therefore, The findings showed that among the 10 main indicators, the criteria of simplicity, Error management (recovery) and language scored 100%, 98.13% and 97.51%, respectively. The criteria of User control and Searching had the lowest compliance with the components of the evaluation list with 21.9% and 26.83%, respectively.Among the statistical population. Niavaran Museum site had the highest level of quality compliance with 71.71% . National Museum of the Islamic Revolution and Holy Defense with 68.55%, Iran national Museum with 68.03%, Iranian National Museum of Medical Sciences History with 67.96%, Razavi Museum with 67.7%, Malek Museum with 66.73%, Sa'dabad Museum Complex with 66.73%, Golestan Museum with 66.59%, Tehran Museum of Contemporary Art with 55.27%, Iran Communication Museum with 42.52 were placed in the next positions.Iran Communication Museum had the lowest level of quality compliance with the evaluation list with 42.52%.
Conclusion
Friedman test is one of the famous non-parametric tests that was used to determine the order of importance of the factors mentioned in the research and to rank the sites. In Friedman test, since the answers are interdependent, comparison can be made in terms of rank. For this reason, Friedman test was used to rank the websites of the studied museums in compliance with the criteria. The results of the investigation of the Friedman test showed that Niavaran Museum Website with a graded average 6.65, National Museum of the Islamic Revolution and Holy Defense Website with a graded average 6.5, Iranian National Museum of Medical Sciences History Website with a graded average 6.45, Razavi Museum Website with a graded average 6.35, Iran national Museum Website with a graded average 6.1, Malek Museum Website with a graded average 5.8, Sa'dabad Museum Complex Website with a graded average 5.8, Golestan Museum Website with a graded average 5.75, Tehran Museum of Contemporary Art Website with a graded average 4 and Iran Communication Museum Website with a graded average 1.6 won the first to 10th rank respectively. Results Wilcoxon signed-rank test showed Among the ten indicators of the user interface evaluation list in the studied museum websites, Simplicity Criterion fully complied; Criteria: Guidance, presentation, navigation on average, more than 50% have been observed; criterias: Searching, Consistency, Design, Language, User control, Error management (recovery)on average, less than 50% have been observed. The results of the investigation of the research hypothesis showed that the studied museum sites differ in respect of user interface evaluation indicators, and the websites of the studied museums have acted differently in terms of compliance with the indicators. Therefore, it was concluded that the user interface designers of each of the sites did not have similar approaches. In order to achieve unity and success as much as possible, it is suggested that a working group be formed to share knowledge and skills among the relevant officials of the studied museums, so as to improve the existing situation. The evaluation list presented in the current research can be adapted for other Iranian museum websites and can be considered by the stakeholders as a proposed model of the user interface.
Afshin Motaghi Destenaei, Ali Karami, Milad Piri Fath Abad,
Volume 11, Issue 2 (9-2024)
Volume 11, Issue 2 (9-2024)
Abstract
Introduction
The idea of creating smart machines and artificial intelligence has been around for centuries and dates back to at least the 14th century. Although the application of artificial intelligence in education is a very new field, but during the last 25 years, artificial intelligence has made achievements in some fields. Which has also affected education of course, criticisms have also been raised against excessive optimism towards contemporary artificial intelligence research. Little research has been done on the expectations of the role of artificial intelligence in education and its potential impact on education. The purpose of this study is to analyze and investigate the role of artificial intelligence in education.
Methods and Materoal
This study was done using SWOT analysis method and its data collection method is also a library
Resultss and Discussion
Text In general, artificial intelligence as a catalyst for teaching and learning with the help of computers is a field with many applications. The teaching of science, technology, engineering and mathematics subjects can be enhanced with artificial intelligence-based software systems. Another potential strength is the potential of AI systems to serve learners across schools, borders, and platforms in creating ecosystems of interactive learning tools. Additionally, AI systems in education may be used to evaluate different learning models throughout the school. Without strong artificial intelligence, tutoring systems cannot provide rapid feedback to learners and enable stimulating interaction. With a realistic view, weak to moderate and strong artificial intelligence have a good ability to support teaching and learning and facilitate the daily work of teachers.
Intelligent learning systems often have less artificial intelligence than expected, especially when it comes to interacting with students. Baker (2016) in a critical position classified many of the existing education systems under stupid education systems. His concept for online learning is to enhance data-driven human intelligence rather than data-driven artificial intelligence. In order to more dynamically use AI in education, there is a need for training data, one of the problems that arise is how to ensure that the data is real and free from bias. As stated by Popenici and Kerr (2017), complex AI algorithms are designed by human programmers who are likely to include their own agendas or biases in the development of the system. An important aspect of high-level machine intelligence is that it customizes learning for each student, but in doing so it intervenes by standardizing content and what is expected of the student.
As reviewed by Lakin et al. (2016), it is hard to see a future where teachers are replaced by artificial intelligence systems or robots. A more positive and realistic scenario is that the role of the teacher evolves and transforms, freeing teachers from tedious daily tasks. In addition, AI in education has the potential to relieve the teacher of the burden of having all the knowledge and information that can be relevant to students. A possible use of artificial intelligence in education in the future is in the form of robots (collaborative robots) that help teachers in their daily work and tailor the learning experience to each student, for example in recording and analyzing the work of these students. And report to the teacher. The use of intelligent learning systems can provide customized instruction or instant feedback to students at any time of the day. But the depth of customization is one of the truly critical features, not superficial and personalized learning. Studies show that developers of intelligent instructional systems have been successful in their goal of adapting and surpassing computer-assisted instruction (CAI) and human teacher training in raising student test scores.
The negative change in the role of the teacher may be caused by the design of stereotypical courses with low-level multiple-choice questions and the use of teachers as content developers. Most school curricula and teacher training programs are not well prepared to take advantage of the benefits of artificial intelligence in education due to not providing artificial intelligence courses to their teachers. If teachers are not trained in the use of artificial intelligence, this can lead to misuse of the technology, for example in protecting privacy and using personal data for influence. According to Nicholas and Holmes (2018), an ethical framework should be established for the use of artificial intelligence in education, and even if adopted, it should be continuously discussed and updated to allow for the capabilities and scope of artificial intelligence and the potential use of reflect it. A growing concern among many education workers is the fear of unemployment as high-level machine intelligence systems completely take over the teaching profession. According to Popenici and Kerr (2017), artificial intelligence currently has the potential to replace a large number of teaching assistants and administrative staff in education, and therefore it is more important to investigate its impact on education. Studies show that widespread use of high-level AI systems may disrupt students' ability to learn independently and develop 21st century skills such as problem solving and critical thinking. Finally, the most severe threat to students may be AI. Surveillance cameras with built-in facial recognition. Along with machine learning, facial recognition is one area where AI is advancing much faster than AI ethics. By using this technology, schools may collect students' biometric information, for example, under the pretext of reducing the many working hours that employees spend on registration and attendance. Support using artificial intelligence systems in education and robotics is certainly an opportunity, but social robots are still in their infancy and have limited social skills. In the near future, a realistic opportunity lies in the development of robots that can provide personalized content and rapid feedback. As in the manufacturing industry, teachers will soon be able to reprogram the cobots using block programming code that doesn't require advanced programming skills. Of course, there are also threats, and for purely economic reasons, we will probably experience cases where teachers are replaced by artificial intelligence solutions in education. Universities with financial problems may be tempted to try solutions, such as Deakin University in Australia, which offers a service where any student who asks can expect tailored information and advice. However, since the common concern is how to submit assignments and how to pay for parking, such systems pose a threat to administrative staff rather than teachers. Finally, as with AI in general, ethics is a major and immediate challenge in the use of AI in education, even though the threats posed by AI in education may not be as dramatic as in other AI areas. Automatic will not be useful. Quality teaching is a complex and creative profession involving improvisation and spontaneity where humans are not easily replaced. In general evaluation, it can be said that there are many ways that artificial intelligence can help students. From identifying signs of effort to creating a more interactive and personalized learning program.
Here are four ways that artificial intelligence can have a positive impact on student learning; Personalized learning: The ability to respond to personalized learning needs is one of the most positive benefits of artificial intelligence in education. Artificial intelligence technology can easily adapt to different learning styles. AI technology can analyze students' past performance and create tailored curricula and settings based on past performance. When it comes to personalized learning, AI can also point students in the right direction for resources and other useful data and information. Artificial intelligence has the ability to provide personalized study plans for students without having to wait for interventions from learning professionals. All while meeting the overall goal of making learning easier and helping students engage with content more effectively. Ultimately, where AI really helps personalized learning is in its ability to reach students on a massive scale. With overcrowded classrooms at the elementary school level and classrooms of hundreds at the secondary level, AI can help personalize education for all students at once, making it easier for everyone to succeed. Tutoring: Sometimes students need extra help, and AI allows you to access on-demand tutoring without an in-person or live tutoring session. Because the AI uses algorithms to adapt, it can quickly change to cover the areas where students need the most support. Just like a human tutor who adapts to a student's learning style and ability to absorb information, AI tutoring systems are very useful in their ability to focus on improving and deepening student learning as a whole. The main advantage of AI-based tutoring technology is the ability to help students understand complex concepts and terms on a mass level. Finally, with artificial intelligence, access to tutoring is no longer limited to those who can afford it. In addition, instructors can spend less time helping those who do not understand the concepts. Assessment and grading: A large part of teachers' time is spent grading assignments. Artificial intelligence technology can help speed up this process. Additionally, when it comes to grading assignments, AI technology can help analyze and get feedback from students on things like grammar, content, and vocabulary. By removing this part of teachers' duties, they can focus on other aspects of teaching that are more important, such as lesson planning and student engagement. Finally, one of the biggest benefits of automated assessment is that it eliminates human error, biases, and mistakes. It can also give each student an outline of where they went wrong and how they can improve, without taking up extra time from teachers. Improving student interaction: Artificial intelligence can engage students in educational content and make learning more interesting. One of the ways that educators and teachers can incorporate artificial intelligence into the classroom is through the use of catboats. The ability of catboats to personalize and adapt to students' learning styles creates more opportunities to keep students engaged, and the fact that catboats can be accessed anytime or anywhere means that students they can work at their own pace and continue their learning outside of traditional classroom time. The fact that AI improves engagement is exciting for course planners and administrators. This means they can deliver highly personalized and interactive learning in their courses, regardless of the subject, helping to amplify the impact on people's lives. Discussed how artificial intelligence can be useful for students. In addition there is great potential impact on coaches and teachers – particularly in ways it can save time.
The three advantages of artificial intelligence in education for teachers are: 1- Predictive analysis an interesting and emerging area of artificial intelligence in education is prediction. AI can analyze data and predict which students might fall behind due to the educational gap. Predictive analytics is exciting for educators because it means students struggling with learning challenges can be identified earlier and given the tools they need to succeed. Additionally, early intervention means that students who otherwise fail or struggle might have the opportunity to become successful students by giving them the right tools to help them succeed. 2-Advanced educational methods one of the methods of using artificial intelligence in education is to improve teaching methods. Today, due to the vast amount of content and information, teachers often have little time to organize alternative learning methods without spending more than hours of classroom time. Using artificial intelligence technology, teachers have the ability to quickly put together games and simulations that help students practice and learn the lessons being taught without spending more time on lesson planning. It saves a lot of time for teachers. 3- Facilitating evaluations and grading if you ask any teacher, they will tell you that assessment is one of the most time-consuming parts of the job. One of the exciting areas of artificial intelligence in education is the use of artificial intelligence technology to improve and speed up the assessment and grading process. For example, assessments can be done in real time instead of lengthy home marking. This not only saves time for teachers, but also improves students' understanding of the material in the moment.
Conclusion
The research findings show that there are both opportunities and threats regarding the role of artificial intelligence in contemporary education. In many ways, AI appears to have a promotional mode. But like other areas of advertising, it has the potential to grow with specific applications in educational and learning activities. The results of the research show that the awareness of artificial intelligence and the study of the role of artificial intelligence in education will reduce the risk of substituting artificial intelligence instead of using artificial intelligence in education
The idea of creating smart machines and artificial intelligence has been around for centuries and dates back to at least the 14th century. Although the application of artificial intelligence in education is a very new field, but during the last 25 years, artificial intelligence has made achievements in some fields. Which has also affected education of course, criticisms have also been raised against excessive optimism towards contemporary artificial intelligence research. Little research has been done on the expectations of the role of artificial intelligence in education and its potential impact on education. The purpose of this study is to analyze and investigate the role of artificial intelligence in education.
Methods and Materoal
This study was done using SWOT analysis method and its data collection method is also a library
Resultss and Discussion
Text In general, artificial intelligence as a catalyst for teaching and learning with the help of computers is a field with many applications. The teaching of science, technology, engineering and mathematics subjects can be enhanced with artificial intelligence-based software systems. Another potential strength is the potential of AI systems to serve learners across schools, borders, and platforms in creating ecosystems of interactive learning tools. Additionally, AI systems in education may be used to evaluate different learning models throughout the school. Without strong artificial intelligence, tutoring systems cannot provide rapid feedback to learners and enable stimulating interaction. With a realistic view, weak to moderate and strong artificial intelligence have a good ability to support teaching and learning and facilitate the daily work of teachers.
Intelligent learning systems often have less artificial intelligence than expected, especially when it comes to interacting with students. Baker (2016) in a critical position classified many of the existing education systems under stupid education systems. His concept for online learning is to enhance data-driven human intelligence rather than data-driven artificial intelligence. In order to more dynamically use AI in education, there is a need for training data, one of the problems that arise is how to ensure that the data is real and free from bias. As stated by Popenici and Kerr (2017), complex AI algorithms are designed by human programmers who are likely to include their own agendas or biases in the development of the system. An important aspect of high-level machine intelligence is that it customizes learning for each student, but in doing so it intervenes by standardizing content and what is expected of the student.
As reviewed by Lakin et al. (2016), it is hard to see a future where teachers are replaced by artificial intelligence systems or robots. A more positive and realistic scenario is that the role of the teacher evolves and transforms, freeing teachers from tedious daily tasks. In addition, AI in education has the potential to relieve the teacher of the burden of having all the knowledge and information that can be relevant to students. A possible use of artificial intelligence in education in the future is in the form of robots (collaborative robots) that help teachers in their daily work and tailor the learning experience to each student, for example in recording and analyzing the work of these students. And report to the teacher. The use of intelligent learning systems can provide customized instruction or instant feedback to students at any time of the day. But the depth of customization is one of the truly critical features, not superficial and personalized learning. Studies show that developers of intelligent instructional systems have been successful in their goal of adapting and surpassing computer-assisted instruction (CAI) and human teacher training in raising student test scores.
The negative change in the role of the teacher may be caused by the design of stereotypical courses with low-level multiple-choice questions and the use of teachers as content developers. Most school curricula and teacher training programs are not well prepared to take advantage of the benefits of artificial intelligence in education due to not providing artificial intelligence courses to their teachers. If teachers are not trained in the use of artificial intelligence, this can lead to misuse of the technology, for example in protecting privacy and using personal data for influence. According to Nicholas and Holmes (2018), an ethical framework should be established for the use of artificial intelligence in education, and even if adopted, it should be continuously discussed and updated to allow for the capabilities and scope of artificial intelligence and the potential use of reflect it. A growing concern among many education workers is the fear of unemployment as high-level machine intelligence systems completely take over the teaching profession. According to Popenici and Kerr (2017), artificial intelligence currently has the potential to replace a large number of teaching assistants and administrative staff in education, and therefore it is more important to investigate its impact on education. Studies show that widespread use of high-level AI systems may disrupt students' ability to learn independently and develop 21st century skills such as problem solving and critical thinking. Finally, the most severe threat to students may be AI. Surveillance cameras with built-in facial recognition. Along with machine learning, facial recognition is one area where AI is advancing much faster than AI ethics. By using this technology, schools may collect students' biometric information, for example, under the pretext of reducing the many working hours that employees spend on registration and attendance. Support using artificial intelligence systems in education and robotics is certainly an opportunity, but social robots are still in their infancy and have limited social skills. In the near future, a realistic opportunity lies in the development of robots that can provide personalized content and rapid feedback. As in the manufacturing industry, teachers will soon be able to reprogram the cobots using block programming code that doesn't require advanced programming skills. Of course, there are also threats, and for purely economic reasons, we will probably experience cases where teachers are replaced by artificial intelligence solutions in education. Universities with financial problems may be tempted to try solutions, such as Deakin University in Australia, which offers a service where any student who asks can expect tailored information and advice. However, since the common concern is how to submit assignments and how to pay for parking, such systems pose a threat to administrative staff rather than teachers. Finally, as with AI in general, ethics is a major and immediate challenge in the use of AI in education, even though the threats posed by AI in education may not be as dramatic as in other AI areas. Automatic will not be useful. Quality teaching is a complex and creative profession involving improvisation and spontaneity where humans are not easily replaced. In general evaluation, it can be said that there are many ways that artificial intelligence can help students. From identifying signs of effort to creating a more interactive and personalized learning program.
Here are four ways that artificial intelligence can have a positive impact on student learning; Personalized learning: The ability to respond to personalized learning needs is one of the most positive benefits of artificial intelligence in education. Artificial intelligence technology can easily adapt to different learning styles. AI technology can analyze students' past performance and create tailored curricula and settings based on past performance. When it comes to personalized learning, AI can also point students in the right direction for resources and other useful data and information. Artificial intelligence has the ability to provide personalized study plans for students without having to wait for interventions from learning professionals. All while meeting the overall goal of making learning easier and helping students engage with content more effectively. Ultimately, where AI really helps personalized learning is in its ability to reach students on a massive scale. With overcrowded classrooms at the elementary school level and classrooms of hundreds at the secondary level, AI can help personalize education for all students at once, making it easier for everyone to succeed. Tutoring: Sometimes students need extra help, and AI allows you to access on-demand tutoring without an in-person or live tutoring session. Because the AI uses algorithms to adapt, it can quickly change to cover the areas where students need the most support. Just like a human tutor who adapts to a student's learning style and ability to absorb information, AI tutoring systems are very useful in their ability to focus on improving and deepening student learning as a whole. The main advantage of AI-based tutoring technology is the ability to help students understand complex concepts and terms on a mass level. Finally, with artificial intelligence, access to tutoring is no longer limited to those who can afford it. In addition, instructors can spend less time helping those who do not understand the concepts. Assessment and grading: A large part of teachers' time is spent grading assignments. Artificial intelligence technology can help speed up this process. Additionally, when it comes to grading assignments, AI technology can help analyze and get feedback from students on things like grammar, content, and vocabulary. By removing this part of teachers' duties, they can focus on other aspects of teaching that are more important, such as lesson planning and student engagement. Finally, one of the biggest benefits of automated assessment is that it eliminates human error, biases, and mistakes. It can also give each student an outline of where they went wrong and how they can improve, without taking up extra time from teachers. Improving student interaction: Artificial intelligence can engage students in educational content and make learning more interesting. One of the ways that educators and teachers can incorporate artificial intelligence into the classroom is through the use of catboats. The ability of catboats to personalize and adapt to students' learning styles creates more opportunities to keep students engaged, and the fact that catboats can be accessed anytime or anywhere means that students they can work at their own pace and continue their learning outside of traditional classroom time. The fact that AI improves engagement is exciting for course planners and administrators. This means they can deliver highly personalized and interactive learning in their courses, regardless of the subject, helping to amplify the impact on people's lives. Discussed how artificial intelligence can be useful for students. In addition there is great potential impact on coaches and teachers – particularly in ways it can save time.
The three advantages of artificial intelligence in education for teachers are: 1- Predictive analysis an interesting and emerging area of artificial intelligence in education is prediction. AI can analyze data and predict which students might fall behind due to the educational gap. Predictive analytics is exciting for educators because it means students struggling with learning challenges can be identified earlier and given the tools they need to succeed. Additionally, early intervention means that students who otherwise fail or struggle might have the opportunity to become successful students by giving them the right tools to help them succeed. 2-Advanced educational methods one of the methods of using artificial intelligence in education is to improve teaching methods. Today, due to the vast amount of content and information, teachers often have little time to organize alternative learning methods without spending more than hours of classroom time. Using artificial intelligence technology, teachers have the ability to quickly put together games and simulations that help students practice and learn the lessons being taught without spending more time on lesson planning. It saves a lot of time for teachers. 3- Facilitating evaluations and grading if you ask any teacher, they will tell you that assessment is one of the most time-consuming parts of the job. One of the exciting areas of artificial intelligence in education is the use of artificial intelligence technology to improve and speed up the assessment and grading process. For example, assessments can be done in real time instead of lengthy home marking. This not only saves time for teachers, but also improves students' understanding of the material in the moment.
Conclusion
The research findings show that there are both opportunities and threats regarding the role of artificial intelligence in contemporary education. In many ways, AI appears to have a promotional mode. But like other areas of advertising, it has the potential to grow with specific applications in educational and learning activities. The results of the research show that the awareness of artificial intelligence and the study of the role of artificial intelligence in education will reduce the risk of substituting artificial intelligence instead of using artificial intelligence in education
Mahdi Akbari Golzar, Dr Ahmad Naderi,
Volume 11, Issue 2 (9-2024)
Volume 11, Issue 2 (9-2024)
Abstract
Introduction
Blockchain technology was first introduced in 2008 as a peer-to-peer electronic payment system. This technology has since attracted widespread attention in the field of scientific research as well as industry. Blockchain has been examined from various aspects. For example, a body of research examines how blockchain's decentralized approach could completely disrupt current business models, financial systems, organizations, and civil governance. Arguably, the clearest evidence of the growth and pervasiveness of this technology is the combined blockchain market capitalization reaching more than 2.6 trillion cryptocurrencies in 2024. In addition, development activity has been steadily growing over the past decade, and numerous projects have been launched to improve the core design of the blockchain (Bitcoin) (such as Ethereum, Kava, and Solana blockchains, etc.). Several articles have systematically reviewed the studies conducted in the field of blockchain in the country using the meta-combination method, all of which focus on the review of foreign articles. Due to the growth and widespread use of blockchain technology in the country and the increase in the scope of domestic research related to it, a systematic review of the research conducted inside the country also seemed necessary. In this regard, the aim of this article is to systematically review internal articles in the blockchain field, focusing on the human-computer interaction (HCI) field of study.
Methods and Materoal
In this research, we have used the qualitative meta-method for a systematic review of blockchain research. A systematic review is a method of identifying, evaluating, and interpreting past research related to a research question, topic area, or phenomenon of interest. The focus of this review is to summarize the HCI literature on blockchain technology. We organized this literature review in four comprehensive steps, following the PRISMA systematic review protocol.
Resultss and Discussion
We found that the articles in our sample adopted one of the following two perspectives. They conducted their research either on blockchain technology (74 articles, 66%) or specifically on cryptocurrencies (37 articles, 34%). Articles related to blockchain technology mainly discuss the understanding of users' motivation, perceived risks and the application of this technology, and articles related to cryptocurrencies also deal more with the jurisprudential and legal aspects of cryptocurrencies and the analysis of transaction risks and user experience. Most empirical studies that deal with people evolve around cryptocurrency, while contributions to blockchain often lead to products or evaluations of financial and administrative systems.
After providing an overview of blockchain research in the HCI community, we present and discuss the salient themes that emerged from the literature review. We identified 4 main themes:
Blockchain technology was first introduced in 2008 as a peer-to-peer electronic payment system. This technology has since attracted widespread attention in the field of scientific research as well as industry. Blockchain has been examined from various aspects. For example, a body of research examines how blockchain's decentralized approach could completely disrupt current business models, financial systems, organizations, and civil governance. Arguably, the clearest evidence of the growth and pervasiveness of this technology is the combined blockchain market capitalization reaching more than 2.6 trillion cryptocurrencies in 2024. In addition, development activity has been steadily growing over the past decade, and numerous projects have been launched to improve the core design of the blockchain (Bitcoin) (such as Ethereum, Kava, and Solana blockchains, etc.). Several articles have systematically reviewed the studies conducted in the field of blockchain in the country using the meta-combination method, all of which focus on the review of foreign articles. Due to the growth and widespread use of blockchain technology in the country and the increase in the scope of domestic research related to it, a systematic review of the research conducted inside the country also seemed necessary. In this regard, the aim of this article is to systematically review internal articles in the blockchain field, focusing on the human-computer interaction (HCI) field of study.
Methods and Materoal
In this research, we have used the qualitative meta-method for a systematic review of blockchain research. A systematic review is a method of identifying, evaluating, and interpreting past research related to a research question, topic area, or phenomenon of interest. The focus of this review is to summarize the HCI literature on blockchain technology. We organized this literature review in four comprehensive steps, following the PRISMA systematic review protocol.
Resultss and Discussion
We found that the articles in our sample adopted one of the following two perspectives. They conducted their research either on blockchain technology (74 articles, 66%) or specifically on cryptocurrencies (37 articles, 34%). Articles related to blockchain technology mainly discuss the understanding of users' motivation, perceived risks and the application of this technology, and articles related to cryptocurrencies also deal more with the jurisprudential and legal aspects of cryptocurrencies and the analysis of transaction risks and user experience. Most empirical studies that deal with people evolve around cryptocurrency, while contributions to blockchain often lead to products or evaluations of financial and administrative systems.
After providing an overview of blockchain research in the HCI community, we present and discuss the salient themes that emerged from the literature review. We identified 4 main themes:
- Decentralized economy and smart contracts (13 articles, 12%)
- Users' understanding and participation of blockchain technology and cryptocurrencies (48 articles, 43%)
- Application of blockchain technology in a specific field (34 articles, 31%)
- Jurisprudence and legal issues around blockchain and cryptocurrencies (16 articles, 14%).
Conclusion
After completing the systematic review of domestic articles, the most interesting point for us is the difference between domestic articles and international topics. As mentioned in some parts of the article, there are three general interests in the international research space that are less observed in domestic research. The first is issues related to the concept of trust in blockchain technologies. The second is the issues related to technical infrastructure and generally the way of socio-technical interactions in society, and the third is related to blockchain-based micro-projects such as Ethereum, Kava, Solana, etc., which are not considered in Iran.
The blockchain ecosystem has experienced rapid growth over the past decade. While until recently, Ethereum was the only widely used blockchain platform supporting decentralized applications, today several new blockchains (such as Solana, Kava, Polkadot, Terra, etc.) have been launched for decentralized applications. Many believe that this new generation of blockchains, which now offer instant transactions with low transaction fees, promises the third generation of the web. Web 1.0 allowed users to read (consume) content on the Internet. Web 2.0 added authoring options and the ability to generate content, thereby enabling rich interactive Internet applications. Powered by blockchain, Web 3.0 now adds the ability to own, create, and distribute digital assets. The first signs of this paradigm shift are the emergence of decentralized finance (DeFi) and non-fungible tokens (NFT), which so far account for more than two-thirds of transactions on the Ethereum blockchain and are driving user adoption of Ethereum. These topics and developments are being noticed by researchers all over the world, but we did not find any study in these fields inside the country. This issue is particularly important from the aspect that Web 3.0 challenges human interaction and cooperation on the Internet and, in a sense, mixes the human and technological space together.The need to pay attention to these research fields as well as the acceptance of interdisciplinary studies (specifically socio-technical studies) should be taken into account in order to open a gate for understanding the fast-paced global technological developments in the space of social studies and a field for presenting theories. To provide a new society in accordance with the socio-cultural context of Iranian society.
After completing the systematic review of domestic articles, the most interesting point for us is the difference between domestic articles and international topics. As mentioned in some parts of the article, there are three general interests in the international research space that are less observed in domestic research. The first is issues related to the concept of trust in blockchain technologies. The second is the issues related to technical infrastructure and generally the way of socio-technical interactions in society, and the third is related to blockchain-based micro-projects such as Ethereum, Kava, Solana, etc., which are not considered in Iran.
The blockchain ecosystem has experienced rapid growth over the past decade. While until recently, Ethereum was the only widely used blockchain platform supporting decentralized applications, today several new blockchains (such as Solana, Kava, Polkadot, Terra, etc.) have been launched for decentralized applications. Many believe that this new generation of blockchains, which now offer instant transactions with low transaction fees, promises the third generation of the web. Web 1.0 allowed users to read (consume) content on the Internet. Web 2.0 added authoring options and the ability to generate content, thereby enabling rich interactive Internet applications. Powered by blockchain, Web 3.0 now adds the ability to own, create, and distribute digital assets. The first signs of this paradigm shift are the emergence of decentralized finance (DeFi) and non-fungible tokens (NFT), which so far account for more than two-thirds of transactions on the Ethereum blockchain and are driving user adoption of Ethereum. These topics and developments are being noticed by researchers all over the world, but we did not find any study in these fields inside the country. This issue is particularly important from the aspect that Web 3.0 challenges human interaction and cooperation on the Internet and, in a sense, mixes the human and technological space together.The need to pay attention to these research fields as well as the acceptance of interdisciplinary studies (specifically socio-technical studies) should be taken into account in order to open a gate for understanding the fast-paced global technological developments in the space of social studies and a field for presenting theories. To provide a new society in accordance with the socio-cultural context of Iranian society.
Hourieh Aarabi Moghaddam, Dr. Alireza Motameni, Dr. Ali Otarkhani,
Volume 11, Issue 2 (9-2024)
Volume 11, Issue 2 (9-2024)
Abstract
Introduction
Governance has always been a key focus throughout history across various levels of authority. The rise and expansion of the financial technology (Fintech) industry have introduced new and diverse challenges for policymakers, highlighting the growing need for an appropriate governance framework. Current global studies on Fintech governance primarily focus on the business and organizational levels, and limited research has been conducted on this topic in Iran. On a macro level, only a few studies have explored the governance of Fintech beyond the enterprise level, although it is seen as a growing field. Therefore, the need for macro-level governance in Fintech is evident both globally and in Iran. This study aims to address the question: What are the governance dimensions and components applicable to the Fintech industry? Based on this, the research seeks to develop a comprehensive framework for governance in Fintech.
Methods and Materoal
This research follows a mixed-methods approach. In the qualitative phase, key terms such as governance, Fintech, and Fintech governance were selected as the foundation for reviewing previous studies. Using meta-synthesis and content analysis, various topics related to governance and Fintech governance were collected and categorized. Data were gathered from the Scopus and Science Direct databases, and studies were filtered based on the relevance of their titles, abstracts, methodologies, and findings. A total of 28 articles were selected for meta-synthesis, and content analysis was conducted to identify governance components relevant to the Fintech industry. Some studies directly addressed governance components applicable to Fintech, while others discussed challenges within Fintech that require governance. Both aspects were incorporated into the proposed framework, leading to an initial framework of governance components for Fintech.
In the quantitative phase, the identified components were validated using the fuzzy Delphi method and potential correlations among them were explored through exploratory factor analysis (EFA). The fuzzy Delphi method was conducted using Excel with input from 15 experts, while EFA was performed using SPSS with data from 217 experts. These experts held advanced degrees in fields such as industrial management, IT management, strategic management, and public administration, with at least five years of experience in governance or Fintech management. Their insights were collected via a standardized questionnaire and analyzed accordingly. Ultimately, the final framework, comprising validated dimensions and components for Fintech governance, was presented.
Resultss
The meta-synthesis of articles on governance, Fintech, and Fintech governance identified seven components: policymaking, foresight, facilitation, regulation, infrastructure development, monitoring, and evaluation. Expert opinions on these seven governance components, as well as on Fintech and Fintech governance, were collected through a standardized fuzzy Delphi questionnaire. Standard fuzzy Delphi calculations were then applied, and the fuzzy values for each component were determined. After fuzzification, a defuzzification process was conducted to convert fuzzy values into definitive ones. The final definitive values for each component were calculated as follows: policymaking (0.82), foresight (0.71), facilitation (0.79), regulation (0.81), infrastructure development (0.71), monitoring (0.77), and evaluation (0.76). According to the fuzzy Delphi method, the acceptable definitive value for each component is 0.7, indicating that all components meet the acceptable threshold, thereby confirming all seven components.
After confirming the components, it was necessary to examine whether any latent internal correlations existed between them, allowing for their reduction into broader factors. To this end, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity were applied to the components based on expert opinions. The KMO value was found to be 0.787, indicating that the components could be reduced to a number of underlying factors and that the sample size was sufficient. Additionally, Bartlett's test showed good correlations among the components within each factor.
To ensure the accuracy of the component categorization, the dimensions were first identified, and each dimension was then named according to the nature of the variables within it. The variance for each component was calculated, and the total variance explained by the extracted dimensions after rotation was determined. These values, known as eigenvalues, indicate the factors that remain in the analysis and the dimensions that can be extracted. Three factors, in total, accounted for 47.7% of the variance across all variables. These three dimensions were named regulation, strategy, and provision. According to Table 1, the components of monitoring and evaluation fell under the "regulation" dimension, the components of policymaking and foresight were grouped under the "strategy" dimension, and the components of facilitation, infrastructure development, and regulation were placed under the "provision" dimension.
Table (1). Rotated Factor Matrix
Governance has always been a key focus throughout history across various levels of authority. The rise and expansion of the financial technology (Fintech) industry have introduced new and diverse challenges for policymakers, highlighting the growing need for an appropriate governance framework. Current global studies on Fintech governance primarily focus on the business and organizational levels, and limited research has been conducted on this topic in Iran. On a macro level, only a few studies have explored the governance of Fintech beyond the enterprise level, although it is seen as a growing field. Therefore, the need for macro-level governance in Fintech is evident both globally and in Iran. This study aims to address the question: What are the governance dimensions and components applicable to the Fintech industry? Based on this, the research seeks to develop a comprehensive framework for governance in Fintech.
Methods and Materoal
This research follows a mixed-methods approach. In the qualitative phase, key terms such as governance, Fintech, and Fintech governance were selected as the foundation for reviewing previous studies. Using meta-synthesis and content analysis, various topics related to governance and Fintech governance were collected and categorized. Data were gathered from the Scopus and Science Direct databases, and studies were filtered based on the relevance of their titles, abstracts, methodologies, and findings. A total of 28 articles were selected for meta-synthesis, and content analysis was conducted to identify governance components relevant to the Fintech industry. Some studies directly addressed governance components applicable to Fintech, while others discussed challenges within Fintech that require governance. Both aspects were incorporated into the proposed framework, leading to an initial framework of governance components for Fintech.
In the quantitative phase, the identified components were validated using the fuzzy Delphi method and potential correlations among them were explored through exploratory factor analysis (EFA). The fuzzy Delphi method was conducted using Excel with input from 15 experts, while EFA was performed using SPSS with data from 217 experts. These experts held advanced degrees in fields such as industrial management, IT management, strategic management, and public administration, with at least five years of experience in governance or Fintech management. Their insights were collected via a standardized questionnaire and analyzed accordingly. Ultimately, the final framework, comprising validated dimensions and components for Fintech governance, was presented.
Resultss
The meta-synthesis of articles on governance, Fintech, and Fintech governance identified seven components: policymaking, foresight, facilitation, regulation, infrastructure development, monitoring, and evaluation. Expert opinions on these seven governance components, as well as on Fintech and Fintech governance, were collected through a standardized fuzzy Delphi questionnaire. Standard fuzzy Delphi calculations were then applied, and the fuzzy values for each component were determined. After fuzzification, a defuzzification process was conducted to convert fuzzy values into definitive ones. The final definitive values for each component were calculated as follows: policymaking (0.82), foresight (0.71), facilitation (0.79), regulation (0.81), infrastructure development (0.71), monitoring (0.77), and evaluation (0.76). According to the fuzzy Delphi method, the acceptable definitive value for each component is 0.7, indicating that all components meet the acceptable threshold, thereby confirming all seven components.
After confirming the components, it was necessary to examine whether any latent internal correlations existed between them, allowing for their reduction into broader factors. To this end, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity were applied to the components based on expert opinions. The KMO value was found to be 0.787, indicating that the components could be reduced to a number of underlying factors and that the sample size was sufficient. Additionally, Bartlett's test showed good correlations among the components within each factor.
To ensure the accuracy of the component categorization, the dimensions were first identified, and each dimension was then named according to the nature of the variables within it. The variance for each component was calculated, and the total variance explained by the extracted dimensions after rotation was determined. These values, known as eigenvalues, indicate the factors that remain in the analysis and the dimensions that can be extracted. Three factors, in total, accounted for 47.7% of the variance across all variables. These three dimensions were named regulation, strategy, and provision. According to Table 1, the components of monitoring and evaluation fell under the "regulation" dimension, the components of policymaking and foresight were grouped under the "strategy" dimension, and the components of facilitation, infrastructure development, and regulation were placed under the "provision" dimension.
Table (1). Rotated Factor Matrix
| Factor (Dimension) | Component | Dimension Name | ||
| 3 | 2 | 1 | ||
| 0.631 | Monitoring | Regulation | ||
| 0.715 | Evaluation | |||
| 0.548 | Policymaking | Strategy | ||
| 0.720 | Foresight | |||
| 0.637 | Facilitation | Provision | ||
| 0.672 | Infrastructure | |||
| 0.437 | Regulation | |||
The consolidation of these dimensions and components of governance for Fintech forms the final framework that this research aims to achieve.
Conclusion and Recommendations
In governance, some tasks are fundamental, while others are specific to the needs of the Fintech industry and must be governed. The integration of these two approaches forms the proposed governance framework. Current discussions on Fintech governance mainly focus on the organizational and business levels, with limited recent research, both in Iran and globally, addressing macro-level governance for Fintech. According to Rostoy (2019), the unique challenges and issues introduced by Fintech require a new form of governance, which strengthens the foundation of this study.
By compiling and summarizing governance components and key issues for Fintech governance, seven components were identified: foresight, policymaking (Taati et al., 2021; Payandeh & Afghahi, 2023), facilitation, regulation (Sharifzadeh & Gholipour, 2003), infrastructure development (Rostoy, 2019), monitoring, and evaluation (Abrahams, 2015). After validating these components, latent correlations between them were identified, resulting in three dimensions: strategy, provision, and regulation. The strategy dimension includes foresight and policymaking, the provision dimension includes facilitation, infrastructure development, and regulation, and the regulation dimension covers monitoring and evaluation. These three dimensions form a cyclical and iterative process, with governance beginning with strategy as its foundation.
Strategic foresight and policymaking are critical to starting the governance process. Policymakers and decision-makers at the national level must implement governance through strategic planning and foresight. The consideration of macro trends and the Fintech industry’s outlook is crucial for governance under the foresight component. Policymaking involves the development of national and sectoral strategies and policies that, together with foresight, form the strategic governance process.
The provision aspect focuses on preparing the governing authorities to foster and support the growth of the Fintech industry. This includes measures such as facilitation, infrastructure development, and regulation. Facilitation, for instance, can be implemented both through soft measures (like legislation) and hard measures (such as platform and system development). The governing body, as the supreme authority, is well-positioned to oversee critical national issues like the economy and national security, thus possessing both the legal and technical power to facilitate Fintech growth. Governance is also evolving toward greater regulation, which is highly relevant and applicable to the Fintech industry.
Finally, in the last phase of the governance cycle, regulation occurs through monitoring and evaluation. To fulfill its duties towards the public good and oversee the performance of Fintech companies, the governing body must monitor the industry and evaluate its performance to ensure accountability and, if necessary, exert control and make corrections. In other words, through regulation, the Fintech industry is held accountable for its performance, and this accountability is achieved through monitoring and evaluation.
Given Iran’s political-economic structure, governance over industries, and the prevailing Islamic laws and regulations, the proposed governance framework for Fintech is applicable to Iran as well. This governance model, with a 360-degree perspective on both the specific challenges of Fintech and the general duties of governance, ensures the alignment of the Fintech industry with Iran’s macroeconomic policies. Furthermore, collaboration and synergy between the Fintech industry and the governing authorities will lead to the growth and development of the sector while ensuring the protection of public interests and citizens' rights. As such, all three pillars of governance, as outlined by Graham et al. (2003), will be balanced: the governing body fulfills its responsibilities toward society, the industry achieves its desired growth, and society benefits from the industry's advancements while safeguarding its rights.
Recommendations for the use and further development of this governance framework are as follows. First, national-level policymakers should expand the seven governance components identified in this study and apply them in accordance with their duties and responsibilities to govern the Fintech industry. Second, clarity in definitions and processes related to each component or dimension will be beneficial for both Fintech and the governing body, helping to avoid many challenges and conflicts in practice, which should be addressed by the governing body as needed. Third, while the authors have endeavored to create comprehensive dimensions and components for governance, there is room for the addition of further components and the extraction of new dimensions. Future researchers are encouraged to explore and expand upon these aspects.
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