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Showing 8 results for Knowledge Sharing
Mohammad Hassanzade, Sakineh Alipour-Irangh, ,
Volume 1, Issue 1 (4-2014)
Volume 1, Issue 1 (4-2014)
Abstract
Background and Aim: The purpose of this study was to investigate the relationship between social capital and knowledge sharing at national information centers in Iran.
Method: This applied research was carried out using two questionnaires and a checklist. Data were collected from all the managers, but stratified random sample of staff members of three: main national information centers (National library, Regional Information Center of Science and Technology [RICEST], and Iranian Institute for Information Science and Technology).
Results: research findings indicated that: A) from managers point of view, lowest relationship between social capital and knowledge sharing belonged to the national library. But results gained from staff members credited the lowest situation to the RICEST; B) all of three information centers coined low range of knowledge sharing, therein, National Library with mean 2.17 out of 5 was the tallest among others; C) National Library was the highest among other centers regarding three dimensions (Relations, Trust and Shared norms) of social capital; D) Relationship between knowledge sharing and trust in all three institutes was significant But in Iranian Institute for Information science and Technology the relationship was more outstanding than others; and E) there was significant relationship between shared norms and knowledge sharing in all institutes.
Conclusion: In order for the National Information centers to improve the knowledge sharing culture among their staff members they should internalize mutual trust, provide them with shared norms and improve organizational communication
Maghsoud Amiri, Ali Entezari, Najmeh Sadat Mortaji,
Volume 3, Issue 3 (12-2016)
Volume 3, Issue 3 (12-2016)
Abstract
Background and Aim: Due to the extensive use of knowledge intelligence, the future of countries depend on the application of specialized knowledge-based social networks. Thus, it is noteworthy to highlight the important role of the professionals. The key indicators of a model for knowledge sharing of Iranian experts, in experts’ social networks has been identified.
Methods: For this purpose, experts were interviewed in depth using a semi-structured framework in the field of research (n = 15) as well as the Delphi method (n = 9) were used to collect data in research.
Results: Findings indicate that the main variable of knowledge sharing behaviors are divided into individual, group, and environmental indexes respectively. Components of the individual dimension of knowledge sharing includes motivation, ethicality, personality, ability, attitude towards knowledge sharing and psychological security. Group dimension of knowledge sharing include shared benefits, group structure and social capital; and finally environmental factors, including technological (beautiful graphics, user-friendly network, server security, the communication infrastructure), political-legal (Funding for R & D for cyberspace area, allocation of financial resources to develop the infrastructure, filtrating, laws relating to intellectual property, bandwidth regulations and laws of cyberspace), economic (The cost of Internet and diversity of online packages) socio-cultural (experts lifestyle and Iranian national character).
Conclusion. We can only come to a comprehensive and theoretical model in the field of knowledge sharing behavior of Iranian users when identify the definitions, concepts, dimensions and components of virtual space based on the conditions prevailing in the country.
Ebrahim Aryani Ghizghapan, Adel Zahed Bablan, Parvaneh Rahimi, Mahdi Moeinikia,
Volume 4, Issue 3 (12-2017)
Volume 4, Issue 3 (12-2017)
Abstract
Background and Aim: The purpose of this study was to explain the mediating role of social capital in the relationship between the application of virtual social network and knowledge sharing practices in cyberspace.
Methods: The research in terms of the main strategy, was quantitative, in terms of the strategy, was field, and in terms of analytical, was descriptive-post-event technique. The statistical population consisted of postgraduate students users of telegrams social network at Mohaghegh Ardabili University in the academic year of 2016-2017. The sampling method was random. The sample size according to the Kregci-Morgan model and with error α = 0.05, was considered 210 persons. To collect data, virtual social networking questionnaire (with reliability α= 0.70), Social Capital Questionnaire of Nahapiet and Ghoshal (1998) (with reliability α= 0.93) and Knowledge Sharing Questionnaire of Bohlool (1392) (with reliability α= 0.93) was used. Validity of the tools was confirmed by the professors of education and psychology. Data were analyzed using two software’s SPSS v. 22 and Lisrel 8.50 and analyzed by structural equation modeling.
Results: The results showed that the proposed model had suitable fit (x2/df=2.96, GFI=0.93, AGFI=0.92, CFI=0.91, NFI=0.93, RMSEA=0.81) and the component of virtual social networking has a direct and indirect effect through the component of social capital on knowledge sharing in cyberspace (P<0.05).
Conclusion: The social network of Telegram, based on its hyperactivity capacity in shaped relationships, has been developing the behavior of user knowledge sharing in the cyberspace. Therefore, educating and developing and continuously monitoring the space of these networks and planning for the future can be a major proposition for virtual domain managers.
Dr Rouhollah Tavallaei, Dr Navid Nezafati, Mr Mohammad Milad Ahmadi,
Volume 6, Issue 1 (4-2019)
Volume 6, Issue 1 (4-2019)
Abstract
Background and Aim: Today, knowledge is essential to the survival and success of any organization. Given that they are the people who create, share and use knowledge, an organization cannot effectively use knowledge unless its employees are willing to share their knowledge and attract knowledge of others. On the other hand, Gamification involves combining the usual mechanisms in the games with the work space, in order to make things more appealing and to direct human behavior towards the goals of the system. Gamification is actually the use of game components on issues other than the game and emphasizes the use of entertainment and pleasure in the work. In this research, it has been tried to study and recognize the Gamification, its dimensions, mechanisms, dynamics, and models, using Grounded Theory method; and the following, we try to find a way to understand the role of the Gamification and apply it to the sharing and dissemination of knowledge.
Methods: Grounded Theory is an inductive method of theoretical discovery that allows the researcher to develop a report on the general characteristics of the subject; While simultaneously consolidating the basis of this report in empirical data observation. Using purposeful sampling, research data were collected using interviewing tools and analyzed through data analysis and coding principles.
Results: In this research, after formulating the stages of Grounded Theory and types of data encoding, in the end, a theory with visual modeling is presented and evaluated.
Conclusion: The dimensions of the proposed framework include the “organizational context”, “game-related dimensions”, and “other indicators that affect the Knowledge Sharing- Gamification”. Each of these dimensions includes lower-level components that are described in the model's description. For example, “organizational context” includes incomes, costs, platform, and so on, and “game-related dimensions” include the mechanisms, dynamics and aesthetics of the game
Elham Mazaheri, Dr Mohammad Mehraeen,
Volume 7, Issue 1 (6-2020)
Volume 7, Issue 1 (6-2020)
Abstract
Aim: The purpose of this paper is to determine the factors affecting the use of enterprise social networks, the types of usage behavior and the consequences of using these networks.
Methodology: This is a descriptive study. The qualitative inquiries of previous researches in the social networking enterprise had been investigated by means of meta-synthesis. A total of 470 found source of meta-synthesis process, 30 papers were consistent with accepted criteria.
Results: As a result of the combination of the findings, 52 codes and 10 main concepts (individual, organizational, social, technical factors and others related to the task, active use and passive use, consequences for the individual level, group level and organizational level) were identified. In conclusion, three categories were identified: factors affecting use, usage behavior and consequences of use that the concepts of individual, organizational, social, technical, and others related to the task as factors affecting use, as well as the concepts of active use and passive use in the category of behavioral usage were classified. In addition, individual, group and organizational level were also classified as concepts related to the significances of use category. Among the three categories, the category of factors influencing use has a greater share of the subject, and consequences categories and its concepts had less attention than the other two categories in preceding researches.
Conclusion: Organizations could employ the results of this study to encourage more employees to use enterprise social networks and thus realize the significances.
Dr Saeedeh Ebrahimy, Dr Ghasem Salimi, Mrs Sahar Anbaraki, Mrs Hanieh Zare,
Volume 7, Issue 1 (6-2020)
Volume 7, Issue 1 (6-2020)
Abstract
Aim: Scientific social networks were shaped as part of a set of social software and a platform for international interactions sharing the tangible and intangible knowledge of researchers. The purpose is to investigate the patterns and behaviors of knowledge sharing of researchers in Research Gate. Based on this, the question and answer system of this scientific social network was analyzed and reviewed.
Methodology: Method is applied descriptive survey with web content analysis approach. The community studied was the questions and answers provided in the Q & A system of Research Gate. Two subject areas were selected, 127 questions and 408 responses related to these two domains were analyzed and reviewed.
Findings: Findings showed that the questions raised in two domains examined in the question and answer system of the scientific network were conceptual questions and replies in both domains were often intangible, and the type of presentation are scientific vision and mental pattern. Most of the inquirers were researchers from Asian countries with less than 5 RG rank. While researchers from European countries - with an RG rank of over 10, have been posting answers and sharing their expertise.
Conclusion: Q & A systems are diverse, efficient technologies for sharing knowledge and enhancing international interactions among researchers beyond the geographical and political boundaries, which has created a place for the reproduction of scientific disciplines in the world. The results according to Castells, speak of latent power of social networks and question and answer systems in knowledge sharing and widespread knowledge boundaries that facilitate transfer of experiences, thought and knowledge of researchers and provide fertile ground for science.
Ali Biranvand, Sanaz Zareei, Maryam Golshani,
Volume 7, Issue 4 (3-2021)
Volume 7, Issue 4 (3-2021)
Abstract
Purpose. The ultimate goal of innovative behavior is to improve performance of the individual, group, and ultimately organization all together. Many factors are influential in the realization of innovative behavior of employees of an organization. In this study, the influence of two factors of organizational climate and knowledge sharing has been reflected.
Method. The study uses an applied descriptive survey method. Population includes all official 373 employees of knowledge-based companies in Fars province (1399). Sample was189 individuals selected by simple random sampling method using Cochran's formula. Data collection was conducted by: Standard Questionnaires of Organizational Climate ( Book et al., 2005), Knowledge Sharing Questionnaire (Hoff and Reader, 2004), and Innovative Behavior Questionnaire (Johnson and Welba, 2004). Confirmatory factor analysis and structural equation modeling tests were used for data analysis.
Findings. The results show that organizational climate has a direct and positive effect on innovative behavior and knowledge sharing. Conversely, knowledge sharing also has a direct and positive effect on innovative behavior. The direct effect of organizational climate on innovative behavior is 0.52, which is strengthened by the role of knowledge sharing and increases by 0.83, which means that knowledge sharing by 0.31 has increased the organizational climate effect on innovative behavior.
Discussion and conclusion. Appropriately understanding - the type of relationship and how to influence organizational climate and knowledge sharing on the innovative performance of employees in knowledge-based companies - managers will be able to take more appropriate measures to instigate innovation in the company to increase organizational productivity, planning and management.
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.
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