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Human Information Interaction 2024, 11(2): 23-47 |
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Rouhi Shalemaie S, Khandan M, Shabani A. Providing An Intergenerational Knowledge Sharing Model For The Car Leasing Industry. Human Information Interaction 2024; 11 (2)
URL: http://hii.khu.ac.ir/article-1-3149-en.html
URL: http://hii.khu.ac.ir/article-1-3149-en.html
Department of Information Science and Knowledge Management, Faculty of Management, University of Tehran, Tehran, Iran
Abstract: (2314 Views)
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.
Keywords: knowledge sharing, intergenerational knowledge, intergenerational knowledge sharing, leasing industry
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