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Dr Alireza Shahraki, Mrs Vajiheh Bahrami,
Volume 0, Issue 0 (5-2022)
Volume 0, Issue 0 (5-2022)
Abstract
Background and Purpose: The IoT is recognized as one of the most efficient and pervasive technologies that is constantly evolving. In order to use it effectively, it is necessary to get acquainted with the capabilities of this technology and the importance of each of them. Therefore, this study was conducted with the aim of identifying and ranking the capabilities of the Internet of Things in the industrial sector using multi-criteria decision-making techniques. And quantitative-qualitative research in terms of data analysis.
Materials and methods: In this study, IoT capabilities were identified in three categories of capabilities, benefits and challenges using library resources and Delphi method through a survey of experts. Data collection was done through questionnaires. Expert Choice software was performed.
Findings: The results of data analysis in this study showed that among the three main criteria, obstacles and challenges, advantages and capabilities are the most important, respectively. Also, among the sub-criteria of obstacles and challenges, security and operating system were the most important and compatibility was the least important. Among the sub-criteria of capabilities, artificial intelligence and communication had the highest and sensors the lowest and weighted rank. Also, among the benefits, saving time and reducing costs were the most important, and process improvement was the least important.
Conclusion: The results of this study showed that in order to use technologies such as the Internet of Things in the manufacturing sector, including the industrial sector, in order to use them more effectively and efficiently, it is necessary to identify the capabilities, advantages and obstacles of this technology. By determining the degree of importance and effectiveness of each of these criteria, selecting and prioritizing that aspect of technology for implementation is determined. Therefore, the results of this study, in addition to identifying the capabilities, advantages and obstacles of using this technology, also identified the priority of each criterion in terms of their importance.
Farideh Osareh, Abdolhossein Farajpahlou, Ms Mansoureh Serati Shirazi,
Volume 3, Issue 3 (12-2016)
Volume 3, Issue 3 (12-2016)
Abstract
Background and Aim: Due to the importance of scientific relations between university and industry, it is so important to identify the factors that affect these relations. So,the aim of this study is to investigate the effect of spatial proximity on university- industry collaboration. The collaboration indicator which is used here is University- Industry Co-publications.
Methods: The research is done by spatial scientometrics aproach and the university- industry co-publications of Iran in the period 2010-2014 from Science Citation Index Expanded of Web of Science were analyzed. In order to to investigate the effect of spatial proximity the Gravity Model was employed. This model for collaboration implays that co-publication between university and industry depens on their total scientific output and the geographical distance between them.
Results: the research findings showed the significant effect of spatial proximity on co-publication.
Conclusion. The findings of this research can be used in research policy making in the way that on the one hand, both university and industry benefit from the co-publication advantages by domestic knowledge flow and on the other hand the rsearchers be able to find propr reseach partner who are not co-located with them
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.
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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